IN VITRO METHOD FOR DETERMINING THE RISK OF SUFFERING FROM

PREECLAMPSIA

FIELD OF THE INVENTION

The present invention refers to the medical field. Particularly, it refers to an in vitro method for the prognosis of preeclampsia as well as for determining the risk of suffering from preeclampsia. The method of the invention is particularly characterized in that it is based on determining the level of expression of genes measured in an endometrial tissue sample obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.

STATE OF THE ART

Preeclampsia (PE) is a severe late pregnancy complication specific to humans. It is the second leading cause of maternal mortality in USA affecting ~8% of first-time pregnancies contributing significantly to neonatal mortality and morbidity. This condition is characterized by the new onset of hypertension, proteinuria and other signs of maternal vascular damage. In severe preeclampsia (sPE) cases women suffers a further elevation of blood pressure (systolic >160 mm Hg or diastolic of >100 mm Hg) or any of the following: thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema and the new onset of cerebral or visual disturbances.

The placenta plays a central role in PE pathophysiology with deficient cytotrophoblast (CTB) invasion of uterine decidua and spiral arterioles producing an incomplete endovascular invasion and altered uteroplacental perfusion. The unsolved question is why shallow CTB invasion occurs. Pregnancy health is determined not only by the embryo -and the placenta- but also by the quality of the maternal decidua, where CTB invasion and remodeling of the maternal spiral arteries occurs. The contribution of the decidua to the etiology of PE, sPE, and placenta accrete has been suggested.

Decidualization is the remodelling of the maternal endometrium initiated after ovulation necessary for adequate trophoblast invasion and subsequent placentation. The formation of the decidua in humans is a conceptus-independent process driven by the progesterone secreted after ovulation and local cyclic Adenosine Monophosphate (cAMP) that through progesterone receptor activation stimulates synthesis of a complex network of intracellular and secreted proteins. Endometrial decidualization involves secretory transformation of the uterine glands, influx of specialized immune cells, vascular remodeling, and the morphological, biochemical and transcriptional reprogramming of the endometrial stromal compartment. Morphologically, is characterized by the transformation of elongated fibroblast-like endometrial stromal cells (ESCs) into enlarged polygonal/round cells shaped by a complex intracellular cytoskeleton rearrangement. Decidualized ESCs secrete biomarkers such as prolactin (PRL) and insulin-like growth factor binding protein- 1 (IGFBP1). Recently, we characterized the transcriptomics of the decidualization process at single cell resolution, discovering that is initiated gradually after ovulation with a direct interplay between stromal fibroblasts and lymphocytes collaborating in the widespread decidualized features by the end of the menstrual cycle.

Defective decidualization entails the inability of the endometrial compartment to undertake tissue differentiation leading to aberrations in trophoblast invasion and placentation, compromising pregnancy health like in sPE. Most of our knowledge about this function in health and disease has been generated from in vitro model systems.

Thus, despite the developments that have been made in this technical field there is still an unmet medical need of finding reliable methods for the identification of sPE. The present invention is focused on solving this problem and a specific preconceptional endometrial transcriptomic signature is herein presented, which is associated with defective decidualization (DD) that might contribute to sPE.

SUMMARY OF THE INVENTION

As explained above, the present invention refers to an in vitro method for the prognosis of preeclampsia. The present invention also refers to an in vitro method for determining the risk of suffering from preeclampsia in a subject.

The present invention also relates to an in vitro method for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, wherein early diagnosis is preconception diagnosis, i.e. diagnosis that is carried out before conception. With the objective of reaching that purpose, we initially performed a global transcriptional profiling of endometrial tissue from patients in whom sPE developed in a previous pregnancy using samples which had been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.

First results using the data analysis approach (a) identified 859 genes differentially expressed in sPE vs control cases. A molecular DD-fmgerprinting including 166 genes was defined associated to the development of sPE, which was tested in an independent cohort of samples. A second analyses using the data analysis approach (b) of the transcriptional profile revelaed 593 genes differentially expressed in sPE vs control cases and a molecular DD-fmgerprinting including 120 genes was defined associated to the development of sPE, which was tested in an independent cohort of samples.

Our analysis revealed the down-regulation of progesterone receptor and its close relation with a high proportion of DD-fmgerprinting genes in sPE. The analysis revealed the down- regulation of estrogen receptor- 1 and its close relation with a high proportion of DD- fmgerprinting genes in sPE patients.

Together, our results suggest that the signature encoding defective decidualization defined in the present invention can be used in a preconceptional stage for in assessing the risk of sPE and the development of therapies focused on improving decidualization.

So, the first embodiment of the present invention refers to an in vitro method for the prognosis of preeclampsia which comprises: a) Measuring the expression level of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle; and b) wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre- established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In another embodiment, the invention refers to a method as defined above wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis. In this sense, it is important to consider that the special technical feature which defines the contribution that the invention makes over the prior art, and consequently confers unity to the present invention, is measuring the level of expression of the genes in a biological sample, preferably endometrial tissue, obtained from the patient 1 to 6 days before the end of the menstrual cycle. This means that any gene included in Table 3 could be used for the prognosis of preeclampsia as far as the expression of the gene is measured in a sample, preferably endometrial tissue, obtained from the patient 1 to 6 days before the end of the menstrual cycle. In this sense, kindly refer to Example 2.7, wherein it is concluded that in the case that biological sample is endometrial tissue, the samples should be collected during late secretory menstrual cycle (1 to 6 before menstrual cycle ends) to detect transcriptional differences between control and preeclampsia that let successful classify the samples into the two groups.

In a preferred embodiment, the method of the invention comprises determining that the patient has a bad prognosis based on a log2fold change of at least 1 for the expression of at least a gene included in Table 3, a log2fold change of at least 2 for the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 for the expression of at least a gene included in Table 9 or a log2 fold change of at least 3 for the expression of at least a gene included in Table 10. In another embodiment, the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.

In a preferred embodiment, the method of the invention comprises: a) Measuring the expression level of all genes comprised in Table 10, or in Table 9, or in Table 8, or in Table 3, or in Table 4, or in Table 3, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b) wherein a deviation in the level of expression of the genes measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In another embodiment, the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis. In a preferred embodiment, the method of the invention comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2fold change of at least 1 for the expression of all genes included in Table 3, a log2fold change of at least 2 for the expression all genes included in Table 8, a log2 fold change of at least 2.5 for the expression all genes included in Table 9 or a log2 fold change of at least 3 for the expression of all genes included in Table 10. In another embodiment, the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.

In a preferred embodiment, the method of the invention comprises: a) Measuring the expression level of progesterone receptor in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b) wherein the determination of a lower the level of expression in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.

In a preferred embodiment, the method of the invention comprises: a) Entering into the computer the level of expression of the genes obtained from healthy control subjects; b) entering into the computer the level of expression of the genes obtained in the step a) of the previous claims; c) producing a score which is displayed on the device; and d) determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with a pre-established threshold level of expression determined in healthy control subjects.

In a preferred embodiment, the biological sample is an endometrial tissue sample.

The second embodiment of the present invention refers to the use in vitro use of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, for the prognosis of preeclampsia, in a biological sample, preferably endometrial tissue, which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle. In another embodiment, the invention refers to the use as defined in this paragraph wherein the use os aimed at the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis. The third embodiment of the present invention refers to a kit for implementing the method of the invention which comprises: a) Reagents for measuring the level of expression of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, and b) tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle.

The fourth embodiment of the present invention refers to the use of the above defined kit for the prognosis of preeclampsia.

The fifth embodiment of the present invention refers to an in vitro method for obtaining information from patients who may be suffering from preeclampsia which comprises: a) Measuring the expression level of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.

The fifth embodiment of the present invention refers to a method for treating preeclampsia which comprises the administration of a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once the patient has been diagnosed by following the above explained method of the invention. Treatments which could be used for treating preeclampsia include:

• Medications to lower blood pressure. These medications, called antihypertensives, are used to lower your blood pressure if it's dangerously high. Blood pressure in the 140/90 millimetres of mercury (mm Hg) range generally isn't treated.

• Corticosteroids. If you have severe preeclampsia or HELLP syndrome, corticosteroid medications can temporarily improve liver and platelet function to help prolong your pregnancy. Corticosteroids can also help your baby's lungs become more mature in as little as 48 hours, an important step in preparing a premature baby for life outside the womb.

• Anticonvulsant medications. If your preeclampsia is severe, your doctor may prescribe an anticonvulsant medication, such as magnesium sulphate, to prevent a first seizure.

For the purpose of the present invention the following terms are defined: • The term "comprising" means including, but it is not limited to, whatever follows the word "comprising". Thus, use of the term "comprising" indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.

• By "consisting of’ means including, and it is limited to, whatever follows the phrase “consisting of’. Thus, the phrase "consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present.

A further aspect of the invention includes an in vitro method for determining the risk of suffering from preeclampsia in a subject, which comprises:

(i) Measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, Table 3 or Table 12 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and

(ii) wherein a deviation in the level of expression of the gene measured in step a) as compared with a reference expression level, is an indication that the patient is at risk of suffering from preeclampsia.

In another aspect, the invention relates to an in vitro method for determining the risk of suffering from preeclampsia, which comprises:

(i) Measuring the expression level of the progesterone receptor or of the estrogen receptor 1 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and

(ii) Wherein reduced expression levels of expression of the progesterone receptor or of the estrogen receptor 1 in step (i) as compared with a pre-established threshold level of expression, is an indication that the patient is at risk of suffering from preeclampsia.

In another aspect, the invention relates to a method for predicting the risk of suffering preeclampsia and treating preeclampsia in a subject which comprises:

(i) Determining the risk of suffering from preeclampsia in said subj ect by a method according the invention,

(ii) Monitoring the subjects which have been determined in step (i) to be at high risk of suffering preeclampsia in order to detect the appearance of preeclampsia and

(iii) Administering to said subject a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once preeclampsia has been detected.

In another aspect, the invention relates to an in vitro use of the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, Table 3 or Table 12 for determining the risk of suffering from preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.

In another aspect, the invention relates to a kit for implementing any of the methods according to the invention, which comprises:

(i) Reagents for measuring the level of expression of at least one gene selected from the genes shown in Table 20, in Table 3, in Table 12 or any combination thereof and

(ii) Tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle.

In yet another aspect, the invention relates to the use of the kit according to the invention for determining the risk of suffering from preeclampsia.

Description of the figures

Figure 1. Global transcriptomics RNAseq results revealed 859 DEGs in sPE. (A) Schematic drawing of the study design used to identify and validate the DD-fmgerprinting in sPE. (B) The statistical significance versus gene expression fold change is displayed as a volcano plot drawing the global RNA-seq results. Threshold indicates: pval-FC (FDR<0.05 and log2-fold- change>l); pval (green dots; FDR<0.05 and log2-fold-change>l); FC (blue dots; FDR>0.05 and log2-fold-change>l) and none (purple dots; FDR>0.05 and log2-fold-change>l). (Q Heatmap showing the 25 most highly up-regulated and down-regulated genes (total= 859;) of control vs. sPE patients.

Figure 2. In vitro vs in vivo decidualization comparison. (A) Venn diagram displaying the number of common genes between previous vitro (left) and current in vivo approach (right). Eighteen genes are overlapping in both approaches. {B) Box plot showing the average expression of the 18 common genes between control (blue boxes) and sPE (orange boxes). ( The fold change between control and sPE was assessed by RT-qPCR (grey bars) and by sequencing (green bars) for 5 transcripts from the 18 transcripts in common in both approaches. (D) Correlation plot for RT-qPCR and RNA seq (method= Pearson, R = 0.99, p = 0.001). RT- qPCR values are expressed Mean± SE. *** P<0.001.

Figure 3. sPE-DD fingerprinting composed by 166 DEGs that are involved in remarkable biologic process. (A) Volcano plot showing the downregulated (blue) and upregulated (red) genes in sPE from the DD-fmgerprinting. Each point represents each gene. Grey points are the not selected genes obtained in the global RNAseq. (B) The five most highly downregulated biological process for each major category of process {red, Immune response; yellow, signaling; green, Extracellular matrix;Z>/we, Cell motility; grey , Blood pressure regulation; purple, Reproductive process) (Q Clustering of DD-fmgerprinting genes within terms is shown for: acute inflammatory response, extracellular matrix disassembly, regulation of systemic blood pressure, response to hormone.

Figure 4. Validation of the DD-fmgerprinting in sPE. (A) Principal component analysis (PCA) based on 166 genes included in the fingerprinting in the training set. Each sample is represented in the Figure as a colored point {blue, Control; orange, sPE). {B) Heatmap dendrogram of expression of the 166 genes included in the final fingerprinting for each sample of the training set (control, n=12; sPE, n=17). (Q Principal component analysis (PCA) based on the fingerprinting in the test set. Each sample is represented in the Figure as a colored point {blue, Control; orange, sPE). (/)) Heatmap dendrogram of expression of the 166 genes included in the final fingerprinting for each sample of the test set (control, n=4; sPE, n=7).

Figure 5. Progesterone receptor is highly connected with DD-fmgerprinting in sPE. {A) Venn diagram displaying the number of genes included in the fingerprinting which are predominantly endometrium expressed and associated with PR, and its overlapping. Genes associated with PR refers to genes with a PR binding site or its expression change if PGR is silenced. {B) Over representation analysis of biological process for the subset of fingerprinting genes which are predominantly expressed in endometrium and associated with PGR (performed using ConsensuspathDB). (Q Gene expression levels of PR, PR-A, and PR-B were assessed for control vs. sPE by RT-qPCR (grey bars, Control; green bars, sPE). RT-qPCR values are expressed Mean± SE. *** P<0.001, ** P<0.01. (D) Progesterone-decidualization network in control pregnancy including the interaction of immune response and endothelium. (E) Progesterone-decidualization network in sPE pregnancy.

Hypothetical network that could link decidualization failure and progesterone receptor. Molecules represented are codified by genes included in the fingerprinting of in vivo decidualization {blue arrow , Downregulated gene; red arrow , upregulated gene). ART and WNT are intermediate genes. PR (Progesterone receptor).

Figure 6. Transcriptomic analysis based on gestational age at delivery of control samples. (A) PCA based on 18476 genes kept after filtering out lowly expressed genes. (B) Volcano plot. Volcano plot threshold at the legend indicates: none (do not have DE genes neither high fold- change); fc (high fold-change, but not DE genes). Plot based on 728 genes labeled as fc (blue) and 17748 genes labeled as none (purple).

Figure 7. Validation of RNA-seq results with nine relevant genes. {A) Boxplot showing the expression patterns of the nine genes selected obtained in RNA-seq for control (blue boxes) and sPE (orange boxes) group. ( B ) RT-qPCR (grey bars) validating the sequencing results (green bars). RT-qPCR values are expressed Mean± SE. *** P<0.001.

Figure 8. PCA based on normalized gene expression. Dot colour represents the endometrial receptivity analysis (ERA) diagnostic for all samples (control and sPE).

Figure 9. PCA based on normalized gene expression. Dot colour represents control (purple) or preeclamptic (yellow) samples.

Figure 10. Global RNA-seq transcriptomic results revealed 593 differentially expressed genes (DEGs) in severe preeclampsia (sPE) vs. control samples. (A) Schematic drawing of the study design used to identify and validate defective decidualization (DD) fingerprinting in sPE. (B) Statistical significance (-loglO FDR) vs. gene expression log2 fold change (FC) is displayed as a volcano plot of global RNA-seq results. Label indicates: downregulated in sPE (blue dots); upregulated in sPE (red dots); not significant genes (grey dots). (C) Heatmap showing the 25 most upregulated and downregulated genes (total = 593; Figure 1 — source data 1) of control vs. sPE samples.

Figure 11. Defective decidualization (DD) transcriptomics in vitro vs. in vivo. (A) Common genes between previous in vitro (left) and current in vivo approaches analyzing decidualization (right). Nine genes overlap in both approaches. (B) Box plot showing the average expression of the nine common genes between control (blue boxes) and severe preeclampsia (sPE) (orange boxes) samples. (C) From the 593 differentially expressed genes (DEGs) obtained by global RNA-seq, a subset of 263 DEGs were identified as genes with a human endometrial stromal cell (hESC) origin using the scRNA-seq data published by Wang et al., 2020.

Figure 12. Severe preeclampsia defective decidualization (sPE-DD) fingerprint composed of 120 differentially expressed genes (DEGs). (A) Volcano plot showing downregulated (blue) and upregulated (red) genes in sPE from the DD fingerprint. Each point represents one gene; gray points are the rest of the genes obtained in the global RNA-seq analysis. (B) The three most highly downregulated biological process for each major category (red, cell cycle; yellow, DNA damage response; green, cell signaling; blue, cellular response; gray, cell motility; purple, extracellular matrix; pink, immune response; brown, reproductive process). Enrichment index was calculated by -log(p-value). (C) Clustering of DD fingerprint genes shown for reproductive process, response to bacterial molecules, extracellular matrix organization, regulation of receptor signaling, and response to hormones.

Figure 13. Validation of the defective decidualization (DD) fingerprint in severe preeclampsia (sPE). (A) Principal component analysis (PCA) based on 120 genes included in the fingerprinting in the training set. Each sample is represented as a colored point (blue, control; orange, sPE). (B) Heatmap dendrogram of expression of the 120 genes included in the final fingerprinting for each sample of the training set (control, n = 12; sPE, n = 17). (C) PCA based on the fingerprinting in the test set. Each sample is represented as a colored point (blue, control; orange, sPE). (D) Heatmap dendrogram of expression of the 120 genes included in the final fingerprinting for each sample of the test set (control, n = 4; sPE, n = 7).

Figure 14. Estrogen receptor 1 (ER1) and progesterone receptor-B (PR-B) are linked to defective decidualization (DD) fingerprinting in severe preeclampsia (sPE). (A) Venn diagram displaying genes included in the fingerprinting (120) predominantly expressed in the endometrium based on Human Protein Atlas data that overlap with genes modulated by ESR1 described by Okur et al., 2016 (58) and genes associated with PGR silencing described by Mazur et al., 2015 (35). (B) Network showing the connections between proteins codified by DD fingerprinting and the hormonal receptors, ER1 and PR. Shapes indicate different clusters established by String k-means method. Squares, cluster involved in gland morphogenesis and cell migration; circles, cluster involved in extracellular matrix organization and stromal cell differentiation; hexagons; cluster involved in cellular response to DNA damage and regulation of cell cycle. Color gradient indicate gene expression in terms of log2FC. Hub genes are shown with an asterisk. (C-H) Gene expression levels of IHH, MSX2, ESR1, PGR, PGR- A, and PGR- B assessed for sPE (n= 13) vs. controls (n=9) by RT-qPCR (gray bars, control; green bars, sPE). RT-qPCR values are expressed as mean± SE. *** p<0.001, ** p<0.01, *p<0.05. (I-J) Tissue sections of control (n=4) and sPE (n=4) endometrium during late secretory phase were immunostained with antibody against ER1 or PR. Nuclei were visualized with DAPI. Scale bar: 50 mM

Detailed description of the invention

The present invention relates to an in vitro method for determining the risk of suffering from preeclampsia. The authors of the invention have found that the risk of suffering from preeclampsia in a patient can be determined by measuring the level of expression of at least one gene in a biological sample of a patient obtained from the patient 1 to 6 days before the end of the menstrual cycle, and comparing this expression level with a reference expression level. The method of the invention allows the identification of women at risk of developing preeclampsia, This may be used to the application of preventive prophylactic measures, medical supervision, medication and treatments before and during pregnancy to reduce maternal and fetal morbidity and mortality.

In vitro method for determining the risk of suffering from preeclampsia

In one aspect, the invention relates to an in vitro method for determining the risk of suffering from preeclampsia, which comprises:

(i) Measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, in Table 3 or in Table 13 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and

(ii) Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression, is an indication that the patient is at risk of suffering from preeclampsia.

The term “risk of suffering from a disease”, in particular preeclampsia, as used herein, refers to a likelihood or probability that a subject develops a clinical condition within a defined time interval. As used herein, the term “determining the risk of suffering from a disease”, in particular, preeclampsia is understood as the assessment of the future clinical status of a patient prior to any sign of disease or symptom in said patient. If a risk of suffering a disease is determined, the clinical status of the patient is likely to change into a status of clinical symptomatology within a given time period after the assessment of the future status of said patient. If the patient is determined not to be at risk of suffering a disease, the clinical status of the patient is likely not to change into a status of clinical symptomatology within a given time period after the assessment of the future status of said patient is determined.

As used herein, the term “suffering from preeclampsia” refers to the appearance of any symptoms or clinical condition related to the placental insufficiency syndrome. Suitable criteria for determining whether a subject suffers from preeclampsia can be found, for instance, in the clinical manual such as Poon LC et al. (The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention. Int J Gynaecol Obstet. 2019 May;145 Suppl l(Suppl 1): 1-33. doi: 10.1002/ijgo.12802. Erratum in: Int J Gynaecol Obstet. 2019 Sep;146(3):390-391. PMID: 31111484; PMCID: PMC6944283) and ACOG Practice Bulletin No. 202: Gestational Hypertension and Preeclampsia. Obstet Gynecol. 2019 Jan; 133(1): 1. doi: 10.1097/AOG.0000000000003018. PMID: 30575675.

The term “suffering from preeclampsia” may also refer to any accompanying clinical signs of preeclampsia including but not limited to hypertension, proteinuria, maternal vascular damage, elevated blood pressure (systolic >160 or diastolic of >100 mm Hg) or thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema, cerebral or visual disturbances, and combinations thereof.

As used herein, the patient is at risk of suffering from preeclampsia with a given sensitivity and specificity if the levels of expression of the at least one gene according to the invention in the sample of the patient deviates from a pre-established threshold level of expression.

As used herein, “determining of the risk of suffering from preeclampsia” in a patient is based on the measurement of the level of expression of at least one gene according to the invention in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle. Accordingly, the method of the invention allows the determination of the risk of suffering preeclampsia before the start of pregnancy. In a first step, the method comprises measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, in Table 3 or in Table 12 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.

The term "expression level”, as used herein, refers to the measurable quantity of gene product produced by the gene in a sample of the subject, wherein the gene product can be a transcriptional product or a translational product. As understood by the person skilled in the art, the gene expression level can be quantified by measuring the messenger RNA levels of said gene or of the protein encoded by said gene. In the context of the present invention, the expression level of the genes used in the method according to the invention can be determined by measuring the levels of mRNA encoded by said gene, or by measuring the levels of the protein encoded by said gene, i.e. the protein or variants thereof. Variants of the proteins encoded by the genes which are measured according to the method of the invention include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained.

The term “sample” or “biological sample”, as used herein, refers to biological material isolated from a subject. The biological sample contains any biological material suitable for detecting DNA, RNA or protein levels. In a particular embodiment, the sample comprises genetic material, e.g., DNA, genomic DNA (gDNA), complementary DNA (cDNA), RNA, heterogeneous nuclear RNA (hnRNA), mRNA, etc., from the subject under study. The sample can be isolated from any suitable tissue or biological fluid such as, for example blood, saliva, plasma, serum, urine, cerebrospinal liquid (CSF), feces, a surgical specimen, a specimen obtained from a biopsy, and a tissue sample embedded in paraffin. In a particular embodiment, the sample from the subject according to the methods of the present invention is an endometrial tissue sample.

Gene expression levels can be quantified by measuring the messenger RNA levels of the gene or of the protein encoded by said gene or of the protein encoded by said gene or of variants thereof. Protein variants include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained. Said term encompasses the protein of any mammal species, including but not being limited to domestic and farm animals (cows, horses, pigs, sheep, goats, dogs, cats or rodents), primates and humans. Preferably, the protein is a human protein.

In order to measure the levels of the mRNA encoded by a given gene, the biological sample may be treated to physically, mechanically or chemically disrupt tissue or cell structure, to release intracellular components into an aqueous or organic solution to prepare nucleic acids for further analysis. The nucleic acids are extracted from the sample by procedures known to the skilled person and commercially available. RNA is then extracted from frozen or fresh samples by any of the methods typical in the art, for example, Sambrook, J., et ah, 2001. Molecular cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, N. Y., Vol. 1-3. In some embodiments, the RNA is extracted from formalin-fixed, paraffin embedded tissues. An exemplary deparaffmization method involves washing the paraffmized sample with an organic solvent, such as xylene, for example. Deparaffmized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example include, methanol, ethanol, propanols, and butanols. Deparaffmized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example. Alternatively, the sample is simultaneously deparaffmised and rehydrated. The sample is then lysed and RNA is extracted from the sample. Commercially available kits may be used for RNA extraction from paraffin samples, such as PureLink™ FFPE Total RNA Isolation Kit (Thermofisher Scientific Inc., US). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (1987) Lab Invest. 56:A67, and De Andres et ak, BioTechniques 18:42044 (1995). Preferably, care is taken to avoid degradation of the RNA during the extraction process.

Various technologies are well-known in the art for deducing and/or measuring and/or detecting the levels of one or more transcripts in a cell. Such methods include hybridization-or sequence- based approaches. Hybridization-based approaches typically involve incubating fluorescently labelled cDNA with custom-made microarrays or commercial high-density oligo microarrays. Specialized microarrays have also been designed; for example, arrays with probes spanning exon junctions can be used to detect and quantify distinct spliced isoforms. Genomic tiling microarrays that represent the genome at high density have been constructed and allow the mapping of transcribed regions to a very high resolution, from several base pairs to -100 bp. Hybridization-based approaches are high throughput and relatively inexpensive, except for high-resolution tiling arrays that interrogate large genomes. However, these methods have several limitations, which include: reliance upon existing knowledge about genome sequence; high background levels owing to cross-hybridization; and a limited dynamic range of detection owing to both background and saturation of signals. Moreover, comparing expression levels across different experiments is often difficult and can require complicated normalization methods.

In contrast to microarray methods, sequence-based approaches directly determine the cDNA sequence. Initially, Sanger sequencing of cDNA or EST libraries was used, but this approach is relatively low throughput, expensive and generally not quantitative. Tag-based methods were developed to overcome these limitations, including serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequencing (MPSS). These tag-based sequencing approaches are high throughput and can provide precise, digital gene expression levels. However, most are based on Sanger sequencing technology, and a significant portion of the short tags cannot be uniquely mapped to the reference genome. Moreover, only a portion of the transcript is analyzed, and isoforms are generally indistinguishable from each other. These disadvantages limit the use of traditional sequencing technology in measuring or detection mRNA levels.

The present methods can also involve a larger-scale analysis of mRNA levels, e.g., the detection of a plurality of biomarkers (e.g., 2-10, or 5-50, or 10-100, or 50-500 or more at one time). In addition, the methods described here can also involve the step of conducting a transcriptomic analysis (i.e., the analysis of the complete set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition). Understanding the transcriptome can be important for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and disease and how the biomarkers disclosed herein are indicative or predictive of a particular condition (e.g., LM or LMS). The key aims of transcriptomics are: to catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs; to determine the transcriptional structure of genes, in terms of their start sites, 5' and 3' ends, splicing patterns and other post-transcriptional modifications; and to quantify the changing expression levels of each transcript during development and under different conditions.

Recently, the development of novel high-throughput DNA sequencing methods has provided a new method for both mapping and quantifying transcriptomes. This method, termed RNA- Seq (RNA sequencing), has advantages over existing approaches for determining transcriptomes. Accordingly, in one embodiment, the expression level of the gene or genes used in the first method of the invention are determined by RNAseq.

As used herein "RNAseq" or"RNA-seq" is used to refer to a transcriptomic approach where the total complement of RNAs from a given sample is isolated and sequenced using high- throughput next generation sequencing (NGS) technologies (e.g., SOLiD, 454, Illumina, or ION Torrent).

RNA-Seq uses deep-sequencing technologies. In general, a population of RNA (total or fractionated, such as poly(A)+) is converted to a library of cDNA fragments with adaptors attached to one or both ends. Each molecule, with or without amplification, is then sequenced in a high-throughput manner to obtain short sequences from one end (single-end sequencing) or both ends (pair-end sequencing). The reads are typically 30-400 bp, depending on the DNA- sequencing technology used. In principle, any high-throughput sequencing technology can be used for RNA-Seq, e.g., the Illumina IG18, Applied Biosystems SOLiD22 and Roche 454 Life Science systems have already been applied for this purpose. The Helicos Biosciences tSMS system is also appropriate and has the added advantage of avoiding amplification of target cDNA. Following sequencing, the resulting reads are either aligned to a reference genome or reference transcripts, or assembled de novo without the genomic sequence to produce a genome-scale transcription map that consists of both the transcriptional structure and/or level of expression for each gene.

Transcriptome analysis by next-generation sequencing (RNA-seq) allows investigation of a transcriptome at unsurpassed resolution. One major benefit is that RNA-seq is independent of a priori knowledge on the sequence under investigation. The transcriptome can be profiled by high throughput techniques including SAGE, microarray, and sequencing of clones from cDNA libraries. For more than a decade, oligo nucleotide microarrays have been the method of choice providing high throughput and affordable costs. However, microarray technology suffers from well- known limitations including insufficient sensitivity for quantifying lower abundant transcripts, narrow dynamic range and biases arising from non-specific hybridizations. Additionally, microarrays are limited to only measuring known/annotated transcripts and often suffer from inaccurate annotations. Sequencing -based methods such as SAGE rely upon cloning and sequencing cDNA fragments. This approach allows quantification of mRNA abundance by counting the number of times cDNA fragments from a corresponding transcript are represented in a given sample, assuming that cDNA fragments sequenced contain sufficient information to identify a transcript. Sequencing-based approaches have a number of significant technical advantages over hybridization- based microarray methods. The output from sequence-based protocols is digital, rather than analog, obviating the need for complex algorithms for data normalization and summarization while allowing for more precise quantification and greater ease of comparison between results obtained from different samples. Consequently, the dynamic range is essentially infinite, if one accumulates enough sequence tags. Sequence-based approaches do not require prior knowledge of the transcriptome and are therefore useful for discovery and annotation of novel transcripts as well as for analysis of poorly annotated genomes. However, until recently the application of sequencing technology in transcriptome profiling has been limited by high cost, by the need to amplify DNA through bacterial cloning, and by the traditional Sanger approach of sequencing by chain termination.

The next-generation sequencing (NGS) technology eliminates some of these barriers, enabling massive parallel sequencing at a high but reasonable cost for small studies. The technology essentially reduces the transcriptome to a series of randomly fragmented segments of a few hundred nucleotides in length. These molecules are amplified by a process that retains spatial clustering of the PCR products, and individual clusters are sequenced in parallel by one of several technologies. Current NGS platforms include the Roche 454 Genome Sequencer, Illumina's Genome Analyzer, and Applied Biosystems' SOLiD. These platforms can analyze tens to hundreds of millions of DNA fragments simultaneously, generate giga-bases of sequence information from a single run, and have revolutionized SAGE and cDNA sequencing technology. For example, the 3' tag Digital Gene Expression (DGE) uses oligo-dT priming for first strand cDNA synthesis, generates libraries that are enriched in the 3' untranslated regions of polyadenylated mRNAs, and produces base cDNA tags.

In various embodiments the use of such sequencing technologies does not require the preparation of sequencing libraries. However, in certain embodiments the sequencing methods contemplated herein requires the preparation of sequencing libraries.

Any method for making high-throughput sequencing libraries can be used. An example of sequencing library preparation is described in U.S. Patent Application Publication No. US 2013/0203606, which is incorporated by reference in its entirety. In some embodiments, this preparation may take the coagulated portion of the sample from the droplet actuator as an assay input. The library preparation process is a ligation-based process, which includes four main operations: (a) blunt-ending, (b) phosphorylating, (c) A-tailing, and (d) ligating adaptors. DNA fragments in a droplet are provided to process the sequencing library. In the blunt-ending operation (a), nucleic acid fragments with 5'- and/or 3 '-overhangs are blunt-ended using T4 DNA polymerase that has both a 3 '-5' exonuclease activity and a 5'-3' polymerase activity, removing overhangs and yielding complementary bases at both ends on DNA fragments. In some embodiments, the T4 DNA polymerase may be provided as a droplet. In the phosphorylation operation (b), T4 polynucleotide kinase may be used to attach a phosphate to the 5'-hydroxyl terminus of the blunt-ended nucleic acid. In some embodiments, the T4 polynucleotide kinase may be provided as a droplet. In the A-tailing operation (c), the 3' hydroxyl end of a dATP is attached to the phosphate on the 5 '-hydroxyl terminus of a blunt- ended fragment catalyzed by exo-Klenow polymerase. In the ligating operation (d), sequencing adaptors are ligated to the A-tail. T4 DNA ligase is used to catalyze the formation of a phosphate bond between the A-tail and the adaptor sequence. In some embodiments involving cfDNA, end-repairing (including blunt-ending and phosphorylation) may be skipped because the cfDNA are naturally fragmented, but the overall process upstream and downstream of end repair is otherwise comparable to processes involving longer strands of DNA.

In another example, sequencing library preparation can involve the production of a random collection of adapter-modified DNA fragments (e.g., polynucleotides) that are ready to be sequenced. Sequencing libraries of polynucleotides can be prepared from DNA or RNA, including equivalents, analogs of either DNA or cDNA, for example, DNA or cDNA that is complementary or copy DNA produced from an RNA template, by the action of reverse transcriptase. The polynucleotides may originate in double-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA, PCR amplification products, and the like) or, in certain embodiments, the polynucleotides may originated in single-stranded form (e.g., ssDNA, RNA, etc.) and have been converted to dsDNA form.

By way of illustration, in certain embodiments, single stranded mRNA molecules may be copied into double-stranded cDNAs suitable for use in preparing a sequencing library. The precise sequence of the primary polynucleotide molecules is generally not material to the method of library preparation, and may be known or unknown. In one embodiment, the polynucleotide molecules are DNA molecules. More particularly, in certain embodiments, the polynucleotide molecules represent the entire genetic complement of an organism or substantially the entire genetic complement of an organism, and are genomic DNA molecules (e.g., cellular DNA, cell free DNA (cfDNA), etc.), that typically include both intron sequence and exon sequence (coding sequence), as well as non-coding regulatory sequences such as promoter and enhancer sequences. In certain embodiments, the primary polynucleotide molecules comprise human genomic DNA molecules, e.g., cfDNA molecules present in peripheral blood of a subject.

Preparation of sequencing libraries for some NGS sequencing platforms is facilitated by the use of polynucleotides comprising a specific range of fragment sizes. Preparation of such libraries typically involves the fragmentation of large polynucleotides (e.g. cellular genomic DNA) to obtain polynucleotides in the desired size range.

In a second step, the method comprises determining whether the patient is at risk of suffering from preeclampsia if the level of expression of the gene measured in the first step deviates with respect to a pre-established threshold level of expression.

A reference gene expression level can be a “threshold” level or a “cut-off’ level. Typically, a “threshold level” or “cut-off level” can be determined experimentally, empirically, or theoretically.

The suitable reference expression levels of the at least one gene according to the invention can be determined by measuring the expression level of said gene in several suitable subjects, and such reference level can be adjusted to specific subject populations. For example, a reference level can be linked to non-pregnant subjects with a history of preeclampsia so that comparisons can be made between expression levels in samples of non-pregnant subjects and reference levels for preeclampsia.

In a particular embodiment, the reference sample may be a pool of samples of endometrial tissue from several individuals.

In a particular embodiment, the pre-established threshold level of expression corresponding to the at least one gene is determined in a sample from a subject or a pool of subjects which have suffered from preeclampsia.

In another embodiment, the pre-established threshold level of expression of the at least one gene is determined in a sample from a subject or a pool of subjects which have not suffered from preeclampsia.

A “pre-established threshold level of expression”, as used herein, may refer to the level of expression of at least one gene determined in a non-pregnant subject who suffered from preeclampsia in a previous pregnancy or to the expression level in a subject who was tested 1 to 6 days before the end of the menstrual cycle and which did not develop preeclampsia during a subsequent pregnancy.

A “pre-established threshold level of expression”, as used herein, may refer to the level of expression of at least one gene determined in non-pregnant subjects who have not suffered preeclampsia in a previous pregnancy.

A “pre-established threshold level of expression”, as used herein, may also refer to the level of expression of at least one gene determined in non-pregnant healthy subjects.

As used herein, a patient which has no symptoms of preeclampsia, but has a high probability to develop clinical symptoms of preeclampsia as pregnancy proceeds, is a patient at risk of suffering from preeclampsia. As used herein, a patient which has no symptoms of preeclampsia and has a low probability to develop clinical symptoms of preeclampsia as pregnancy proceeds, is not a patient at risk of suffering from preeclampsia.

The method of the invention allows for a classification of a subject based on the risk of said subject to suffer from preeclampsia to develop clinical signs related to preeclampsia within a defined time interval. As used herein, a defined time interval may refer to the period of pregnancy. According to the invention, the method for predicting the risk of suffering from preeclampsia allows the classification or selection of a pregnant patient as i) being at risk of suffering from preeclampsia or ii) not being at risk of suffering from preeclampsia.

Once the pre-established threshold level of expression corresponding to the at least one gene is established, the levels of expression of the at least one gene in a subject in which the risk of suffering from preeclampsia is to be determined can be compared with the pre-established threshold level, and thus be assigned as “increased”, “decreased” or “equal”.

In some embodiment, the expression profile of the genes in the reference sample can preferably be generated from a population of two or more individuals. The population, for example, can comprise 3, 4, 5, 10, 15, 20, 30, 40, 50 or more individuals.

According to the present invention, a patient may be classified as having the status of being at risk of suffering from preeclampsia based on the deviation of the level of expression of at least one gene with respect to a pre-established threshold level of expression.

According to the present invention, a patient may be classified as having the status of not being at risk of suffering from preeclampsia based on the deviation of the level of expression of at least one gene according to the invention with respect to a pre-established threshold level of expression.

In some embodiments, a deviation in the level of expression of at least one gene according to the invention may refer to an increase in the level of expression as compared with a pre- established threshold level of expression. In some embodiments, a deviation in the level of expression of at least one gene may refer to a reduction in the level of expression as compared with a pre-established threshold level of expression.

In some embodiments, an increase in the level of expression of at least 1.1-fold, 1.5-fold, 5- fold, 10-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the pre-established threshold level of expression is considered as “increased” level.

In some embodiments, a reduction in the level of expression of at least 1.1-fold, 1.5-fold, 5- fold, 10-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the pre-established threshold level of expression is considered as “reduced” level.

In some embodiments, the term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene may relate to a situation where the level of the at least one gene is increased at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% when compared to the corresponding pre-established threshold level of expression.

In some embodiments, the term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene may relate to a situation where the level of expression of the at least one gene is reduced at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% when compared to the corresponding pre-established threshold level of expression.

Levels can be seen as “equal” to the pre-established threshold level of expression if the levels differ with respect to the pre-established threshold level of expression less than 5%, less than 4%, less than 3%, less than 2%, less than 1%, less than 0.5 %, less than 0.4%, less than 0.3%, less than 0.1%, less than 0.05%, or less.

In a preferred embodiment, the method of the invention is characterized in that the expression level of at least one gene shown in Table 21 is determined. In a preferred embodiment, the method of the invention is characterized in that the expression level of at least one gene shown in Table 4 is determined.

In some embodiments, the deviation in the level of expression of at least one gene according to the invention may refer to a log2fold change.

The term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene according to the invention may relate to a situation where the level of expression of the at least one gene is increased a log2fold of at least 1, a log2fold of at least 1.5, a log2fold of at least 2, a log2fold of at least 2.5, a log2fold of at least 3, a log2fold of at least 5, compared with a pre-established threshold level of expression is considered as “increased” level.

The term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene according to the invention may relate to a situation where the level of expression of the at least one gene is reduced a log2fold of at least 1, a log2fold of at least 1.5, a log2fold of at least 2, a log2fold of at least 2.5, a log2fold of at least 3, a log2fold of at least 5, compared with a pre-established threshold level of expression is considered as “reduced” level.

In another preferred embodiment, the method of the invention is characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 3, a log2fold change of at least 2 in the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 9 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 10.

In another embodiment, the in vitro method is characterized in that the expression levels of all genes comprised in Table 3, in Table 8, in Table 9 and/or, in Table 10. In another embodiment, the in vitro method is characterized in that the expression levels of all genes comprised in Table 13.

In another preferred embodiment, the method of the invention is characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 15, a log2fold change of at least 2 in the expression of at least a gene included in Table 16, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 17 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 18.

In another embodiment, the in vitro method is characterized in that the expression levels of all genes comprised in Table 15, in Table 16, in Table 17, in Table 18, in Table 19.

Method for determining the risk of suffering preeclampsia based on the expression levels of the progesterone receptor or of the estrogen receptor 1

In another aspect, the invention relates to an in vitro method for determining the risk of suffering from preeclampsia comprises:

(i) Measuring the expression level of the progesterone receptor or of the estrogen receptor 1 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and

(ii) Wherein reduced expression levels of expression of the progesterone receptor or of the estrogen receptor 1 in step (i) as compared with a reference expression value, is an indication that the patient is at risk of suffering from preeclampsia.

The terms “determining the risk of suffering from preeclampsia”, “reference value”, “reduced expression”, “biological sample” have been described in detail in respect to the previous method of the invention and are equally applicable to the present method.

The term “progesterone receptor” as used herein, refers to mRNA encoding the isoform B of the progesterone receptor gene and which results from the alternative splicing of the transcript encoded by the progesterone receptor gene which is shown in the HGNC database under accession number 8910, in the NCBI Entrez Gene database under accession number 5241, in the Ensembl database under accession number ENSG00000082175, in the OMIM® database under accession number 607311. The progesterone receptor isoform B polypeptide is shown in the UniProtKB/Swiss-Prot database under accession number P06401-1.

The term “estrogen receptor 1”, as used herein, refers to the gene encoding a nuclear hormone and which is shown in the HGNC database under accession number 3467, in the NCBI database under Entrez Gene number 2099, in the Ensembl database under accession number ENSG00000091831, in the OMIM® database under accession number 133430 and which encodes a polypeptide shown in the UniProtKB/Swiss-Prot database under accession number P03372.

Suitable methods for determining the expression levels of the progesterone receptor or of the estrogen receptor 1 have been described in detail in the previous aspects of the invention and are equally applicable to the present method. In one embodiment, the biological sample is an endometrial tissue sample.

In vitro method for the prognosis of preeclampsia

As used herein, the term “prognosis of preeclampsia” may be understood as the prospect of recovery as anticipated from the usual course of preeclampsia or peculiarities of the case. Also, the term “prognosis” may refer to the likely outcome or course of a disease; the chance of recovery or recurrence. Accordingly, the terms “prognosis of preeclampsia” and “risk of suffering from preeclampsia” as used herein may be understood as synonyms.

In another aspect, the present invention relates to an in vitro method for the prognosis of preeclampsia.

In a first step the method comprises measuring the expression level of at least a gene selected from Table 3, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle. In a second step, the method comprises determining whether the patient is suffering from preeclampsia or has a bad prognosis if the level of expression of the gene measured in the first step deviates with respect to a pre-established threshold level of expression.

In a preferred embodiment, the In vitro method, according to claim 1, characterized in that step

(i) comprises the measurement of the expression level of at least a gene selected from Table 4, or any combination thereof.

Computer implemented methods of the invention

In another aspect, the invention relates to any of the methods defined in the previous aspects in the invention in which the method is computer implemented. The method comprises:

(i) Entering into the computer the reference expression levels of the genes which are measured in step (i);

(ii) Entering into the computer the level of expression of the genes obtained in the step (i) of the previous claims;

(iii) Producing a score which is displayed on the device;

(iv) Determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with the reference expression levels.

Methods of the invention can be performed using software, hardware, firmware, hardwiring, or combinations of any of these.

Methods for the prediction of the risk of suffering from preeclampsia in a subject and subsequent treatment of the subject

In another aspect, the invention provides a method wherein a patient is selected based on showing an increased risk of suffering preeclampsia, said patient is then monitored in any subsequent pregnancy for the appearance of the symptoms of preeclampsia and then, if preeclampsia is detected, the patient is treated with a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia. Thus, in another aspect, the invention relates to a method for predicting the risk of suffering preeclampsia and treating preeclampsia in a subject which comprises:

(i) Determining the risk of suffering from preeclampsia in said subject by a method according to any of methods for predicting the risk of suffering preeclampsia according to the present invention,

(ii) Monitoring the subjects which have been determined in step (i) to be at high risk of suffering preeclampsia in order to detect the appearance of preeclampsia and

(iii) Administering to said subject a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once preeclampsia has been detected.

In another aspect, the invention relates to a therapy useful in the treatment of preeclampsia for use in the treatment of a subject suffering from preeclampsia, wherein the patient was identified as having high risk of suffering preeclampsia by any of the methods according to the invention and then further selected by detecting the appearance of preeclampsia during a pregnancy subsequent to the prediction of the risk of suffering preeclampsia.

The appearance of preeclampsia can be detected in the patients identified as having high risk of suffering preeclampsia by detecting of any of the following:

• severe headaches.

• vision problems, such as blurring or seeing flashing lights.

• severe heartburn.

• pain just below the ribs.

• nausea or vomiting.

• excessive weight gain caused by fluid retention.

• feeling very unwell.

• sudden increase in oedema - swelling of the feet, ankles, face and hands.

Suitable treatments which could be used for treating preeclampsia include:

• Medications to lower blood pressure. These medications, called antihypertensives, are used to lower your blood pressure if it's dangerously high. Blood pressure in the 140/90 millimetres of mercury (mm Hg) range generally isn't treated. • Corticosteroids. If you have severe preeclampsia or HELLP syndrome, corticosteroid medications can temporarily improve liver and platelet function to help prolong your pregnancy. Corticosteroids can also help your baby's lungs become more mature in as little as 48 hours, an important step in preparing a premature baby for life outside the womb.

• Anticonvulsant medications. If your preeclampsia is severe, your doctor may prescribe an anticonvulsant medication, such as magnesium sulphate, to prevent a first seizure.

Kit of the invention and uses thereof

In another aspect, the invention relates to a kit, package or device that contains reagents adequate for implementing any of the methods of the invention. It will be understood that, depending on the nature of the method, the reagents adequate for its implementation will vary.

In the context of the present invention, “kit” is understood as a product containing the different reagents required for carrying out the methods of the invention packaged such that it allows being transported and stored. The materials suitable for the packaging of the components of the kit include glass, plastic (polyethylene, polypropylene, polycarbonate, and the like), bottles, vials, paper, sachets, and the like. Where there are more than one component in a kit they may be packaged together if suitable or the kit will generally contain a second, third or other additional container into which the additional components may be separately placed. However, in some embodiments, certain combinations of components may be packaged together comprised in one container means. A kit can also include a means for containing any reagent containers in close confinement for commercial sale. Such containers may include injection or blow-molded plastic containers into which the desired vials are retained. One or more compositions of a kit can be lyophilized. In some embodiments, all compositions of a kit of the disclosure will be lyophilized. In some embodiments, a kit of the disclosure with one or more lyophilized agents will be supplied with a re-constitution buffer. Reagents and components of kits may be comprised in one or more suitable container means. A container means may generally comprise at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted.

Furthermore, kits according to the invention can also comprise one or more reagents for preparing crude cell lysates and/or reagents for extracting, isolating and/or purification of nucleic acids from a sample. Additional components can comprise particles with affinity for nucleic acids and/or solid supports with affinity for nucleic acids, one or more wash buffers, binding enhancers, binding solutions, polar solvents, alcohols, elution buffers, filter membranes and/or columns for isolation of DNA/RNA. A kit may further comprise reagents for downstream processing of an isolated nucleic acid and may include without limitation at least one RNase inhibitor; at least one cDNA construction reagents (such as reverse transcriptase); one or more reagents for amplification of RNA, one or more reagents for amplification of DNA including primers, reagents for purification of DNA, probes for detection of specific nucleic acids. Furthermore, the kits of the invention can contain instructions for the simultaneous, sequential, or separate use of the different components that are in the kit. Said instructions can be in the form of printed material or in the form of an electronic medium capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes, and the like), optical media (CD-ROM, DVD), and the like. The media may additionally or alternatively contain Internet addresses providing said instructions.

In some embodiments, the kit comprises primers or probes adequate for the detection of the expression levels of one or more of the genes, the expression levels of which are determined in the any of the methods according to the invention.

The term "primer" as used herein refers to oligonucleotides that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the primer within a sequence of the target polynucleotide sequence. A primer can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides. For proper hybridization to the target sequence, a primer can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to the hybridized portion of the target polynucleotide sequence. Oligonucleotides useful as primers may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts. (1981) 22: 1859-1862, using an automated synthesizer, as described in Needham-Van Devanter et al, Nucleic Acids Res. (1984) 12: 6159-6168. Primers are useful in nucleic acid amplification reactions in which the primer is extended to produce a new strand of the polynucleotide. Primers can be readily designed by a skilled artisan using common knowledge known in the art, such that they can specifically anneal to the nucleotide sequence of the target nucleotide sequence of the at least one biomarker provided herein. Usually, the 3' nucleotide of the primer is designed to be complementary to the target sequence at the corresponding nucleotide position, to provide optimal primer extension by a polymerase.

The term "probe" as used herein refers to oligonucleotides or analogs thereof that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the probe within a sequence of the target polynucleotide sequence. Exemplary probes can be, for example DNA probes, RNA probes, or protein nucleic acid (PNA) probes. A probe can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides. For proper hybridization to the target sequence, a probe can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to hybridized portion of the target polynucleotide sequence. Probes can also be chemically synthesized according to the solid phase phosphoramidite triester method as described above. Methods for preparation of DNA and RNA probes, and the conditions for hybridization thereof to target nucleotide sequences, are described in Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition. Cold Spring Harbor Laboratory Press, 1989, Chapters 10 and 11.

In a preferred embodiment, the reagents adequate for the determination of the expression levels of one or more genes comprise at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% of the total amount of reagents adequate for the determination of the expression levels of genes forming the kit.

The present invention is illustrated by means of the Examples below without the intention of limiting its scope of protection.

Example 1. Material and methods Example 1.1. Study Design

A total of 40 non-pregnant women that experienced a previous pregnancy were enrolled in this study for endometrial RNA sequencing analysis. Endometrial samples were obtained during late secretory phase in 24 women that have developed sPE in a previous pregnancy and 16 with no history of sPE with term (n=8) and preterm pregnancies (n=8) previous as controls. Endometrial biopsies were processed to obtain RNA and then converted to cDNA for library generation to perform next generation sequencing (NGS). The experimental design was based on a stratified random sampling with a 70:30 proportion in two cohorts: training (n=29) and validation (n=l 1) set of samples. Training set of samples was analyzed by RNA sequencing to identify the global transcriptomic profiling changes between control (n=12) and sPE (n=17) patients. Selection criteria were applied to define a transcriptomic fingerprinting associated to decidualization defect detected in sPE. Finally, a targeted analysis of the fingerprinting DD signature was validated in the test set composed by controls (n=4) and sPE (n=7).

Example 1.2. Endometrial Sample Collection

Samples were collected from women aged 18-42 without any medical condition who had been pregnant 1-8 years earlier. All patients had regular menstrual cycle (26-32 days), with no underlying gynecological pathologic conditions, and had not received hormonal therapy in the 3 months preceding sample collection. After the inclusion criteria were applied, endometrial biopsies were obtained by pipelle (Genetics Hamont-Achel, Belgium) under sterile conditions in the late secretory (LS) phase (cycle days 22-32). Specimens were kept in preservation solution until processing.

This study was approved by the Clinical Research Ethics Committee of Hospital La Fe (Valencia, Spain) (2011/0383), and written informed consent was obtained from all patients before tissue collection, and all samples were anonymized.

Example 1.3. RNA extraction

Total RNA from endometrial biopsies was isolated using QIAsymphony RNA kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. RNA concentrations were quantified using a Multiskan GO spectrophotometer (Thermo Fisher Scientific, Waltham, CIS) at a wavelength of 260 nm. Integrity of the total RNA samples was evaluated by the RNA integrity number (RIN) and DV200 metrics using an Agilent high-sensitivity RNA ScreenTape in a 4200 TapeStation system (Agilent Technologies Inc., Santa Clara, CA). Samples used for the global RNA-seq showed RIN values ranging from 4.9 to 9.2.

Example 1.4.Global RNAseq library preparation and transcriptome sequencing The cDNA libraries from total RNA samples (n=40) were prepared by an Illumina TruSeq Stranded mRNA sample prep kit (Illumina, San Diego, CA). Three micrograms of total RNA were used as the RNA input according to recommendations of the manufacturer’s protocol. The mRNAs were selected from the total RNAs by purifying the poly-A containing molecules using poly-T oligo attached to magnetic beads. The RNA fragmentation, first and second strand cDNA syntheses, end repair, single ‘A’ base addition, adaptor ligation, and PCR amplification were performed according to the manufacturer’s protocol. The average size of the cDNA libraries was approximately 350 bp (including the adapters). The cDNA libraries were validated for RNA integrity and quantity using an Agilent 4200 TapeStation system (Agilent Technologies Inc., Santa Clara, CA) before pooling the libraries. The pool concentration was quantified by a qPCR using the KAPA Library Quantification Kit (Kapa Biosystems Inc.) before sequencing in a NextSeq 500/550 cartridge of 150 cycles (Illumina, San Diego, CA). Indexed and pooled samples were sequenced 150-bp paired-end reads by on the Illumina NextSeq 500/550 platform, according to Illumina library protocol.

Example 1.5. RNA-seq analysis

Reads were mapped to the hgl9 human genome transcriptome using the STAR (version 2.4.2 a) read aligner (Dobin et al., 2013). FastQC (version 0.11.2) was used to determine the quality of FASTQ files. The manipulation of SAM and BAM files was done with the software SAMtools (version 1.1) (Li et al., 2009). To count the number of reads that could be assigned to each gene, we used HTSeq (version 0.6. lpl; Anders et al., 2015) and BEDtools software (version 2.17.0; Quinlan and Hall, 2010) to obtain gene coverage and work with bedFiles. Quality control filters in each program were used following the software package recommendations, and reads were filtered by mapping quality greater than 90%. Transcriptomic data were deposited in the Gene Expression Omnibus database (accession number GSE172381). The Bioconductor package edgeR (version 3.24.3; Robinson etak, 2010) was used to analyze differentially expressed genes (DEGs). The trimmed mean of M-values normalization method was applied to our gene expression values. Differentially expressed genes were obtained through two approaches. In the first approach, the exactTest function was used and the p-value adjustment method was false discovery rate (FDR) with a cutoff of 0.05 (FDR < 0.05). Once the p-value was adjusted, significant deregulated genes with log2-fold- change > 1 (FC >2) were selected. Differentially expressed genes were obtained through two approaches. In the second approach, the glmTreat function was used to find DEGs between groups. The p-value adjustment method was false discovery rate (FDR) with a cutoff of 0.05 (FDR < 0.05) and the fold-change (FC) threshold was 1.2. edgeR analysis was carried out in R version 3.5.1. A volcano plot was created to visualize DEGs. Custom scripts are available on GitHub at link https://github.com/mclemente-igenomix/garrido_et_al_2021.

Example 1.6. Transcriptomic fingerprinting definition and validation

In the first approach, genes with assigned EntrezID with an FDR cut-off of 0.05 and an expression >4-fold higher in the sPE vs. control training set samples were selected to define a fingerprint associated with DD in sPE. In the second approach, genes with assigned EntrezID with an FDR cutoff of 0.05 and an expression >1.4- fold higher in the sPE vs. control training set samples were selected to define a fingerprint associated with DD in sPE. Targeted analysis of fingerprinting genes was performed using the validation set of samples. PCA and unsupervised hierarchical clustering with a Canberra distance based on gene signature were performed comparing sPE to control specimens. Custom scripts are available on GitHub at https:// github.com/mclemente-igenomix/garrido_et_al_2021.

Example 1.7. Enrichment Analysis

Biological process in which those differentially expressed genes (DEGs) are involved were studied. In edgeR, GO analyses can be conducted using the goana. In the first approach, an FDR cutoff of 5% is used when extracting DE genes and for log2FC we use cut-off value of 1 [UP, log2FC>l and DOWN; log2FC <(-1)]. The ontology domain that GO term belongs to is biological process (BP). As the p-values obtained are not adjusted for multiple testing, we would ignore GO terms with p-values greater than about 0.005. . In the second approach, the input genes were those 120 included in the fingerprinting. The p-value adjustment method was FDR with a cut-off of 0.05 (FDR<0.05). Custom scripts are available on GitHub at https://github.com/mclemente-igenomix/garrido_et_al_2021.

Example 1.8. qRT-PCR Gene Validation.

To validate our transcriptomic results a selection of differentially expressed genes was validated by qRT-PCR in a subgroup of samples from the experimental cohort [controls (n=9) and sPE (n= 14)] . Specific primers for each gene were described in Table 1 (RT-qPCR primers list). The primers included in Table 1 correspond with SEQ ID NO: 1 to 22.

Table 1 Sequence Name Sequence

AOX1 FW TGTCCATCTACACGCTGCTC

AOX1_RV TCCTCAAATTCTGGCAATCC

ERP27_FW ACAAGGCCTCCCCAGAGTAT

ERP27_RV CTTCTGCTGTGGGCAGTGTA

ISM1_FW GACCTGTGACCGTCCAAACT

ISM1_RV AGAACTCGCTTTTGCAGCTC

MEST_FW CGCAGGATCAACCTTCTTTC

MEST_RV CATCAGTCGTGTGAGGATGG

MFAP2_FW CCAGATCGACAACCCAGACT FAP2_RV GCAAGGCCTGTGTATGGAGT

MMP11_FW GGTCTCTGAGGGTCAAGCAG

MMP11_RV AGTTCATGAGCTGCAACACG

PGRMC1_FW CCTCTGCATCTTCCTGCTCT

PGRMC1_RV CGTTGATGGCCATGAGTATG

PGR_FW GTGGGAGCTGTAAGGTCTTCTTTAA

PGR_RV AACGATGCAGTCATTTCTTCCA

PGRB_FW TCGGACACCTTGCCTGAAGT

PGRB_RV CAGGGCCGAGGGAAGAGTAG

REEP2_FW GGGTGCTGTCAGAGAAGCTC

REEP2_RV TGTCTCCCATGTCATCCTCA

WNT5A_FW TGGCTTTGGCCATATTTTTC

WNT5A RV CCGATGTACTGCATGTGGTC cDNA was generated from 400 ng of RNA using the Superscript VILO cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, US). The template cDNA was diluted 5 in 20 and 1 pL was used in each PCR reaction. Real-time PCR was performed in duplicate in 10 pL using commercially validated Kapa SYBR fast qPCR kit (Kapa biosystems Inc, Basilea, Switzerland) and the Lightcycler 480 (Roche Molecular Systems, Inc, Pleasanton, CA) detection system. Samples were run in duplicate along with appropriate controls (i.e. no template, no RT). Cycling conditions were as follows: 95°C for 3 min, 40 cycles of 95°C for 10 s, 60°C for 20 s and 72°C for 1 s. A melting curve was done following the product specifications. Data were analyzed using the comparative Ct method (2-AACT). Data were normalized to the housekeeping gene b-actin, changes in gene expression were calculated using the AACT method with the control group used as the calibrator; values are illustrated relative to median in the control group. Immunofluorescence of tissue sections Endometrial tissue samples were fixed in 4% paraformaldehyde and preserved in paraffin- embedded blocks. For immunostaining, tissue sections were deparaffinated and rehydrated. Antigen retrieval was performed with buffer citrate 1 x at 100 °C for 10 min. Then, non-specific reactivity was blocked by incubation in 5% BSA/0.1% PBS-Tween 20 at room temperature for 30 min. Sections were incu- bated at room temperature for 1.5 hr with primary antibodies (1:50 rabbit monoclonal anti-human progesterone receptor, Abeam, Cambridge, UK) and 1:50 mouse monoclonal anti-human estrogen receptor 1 (Santa Cruz Biotechnology, CA) diluted in 3% BSA/0.1% PBS-Tween 20. Then, slides were washed two times for 10 min with 0.1% PBS-Tween 20 before they were incubated for 1 hr at room temperature with AlexaFluor- conjugated secondary antibodies diluted in 3% BSA/0.1% PBS- Tween 20 (1:1000). Finally, slides were washed two times in 0.1% PBS-Tween 20. To visualize nuclei, 4',6-diamidino-2- phenylindole at 400 ng/pL was used. Tissue sections were examined using a EVOS M5000 microscope.

Example 1.10. Statistical Analysis

Clinical data are expressed as mean ± standard error mean (SEM). Clinical data were evaluated by two statistical methods: Student's t-test (Table 2) and Wilcoxon test (Table 11) for comparisons between sPE and control samples. Statistical significance was set at P-value of <0.05. Differential expression analysis was performed using the R package edgeR.

Example 2. First analysis of the global transcriptional signature of defective decidualization in vivo from sPE patients

Example 2.1. Global transcriptional signature of defective decidualization in vivo from sPE patients

We assessed the decidualization transcriptional status in vivo of endometrial biopsies obtained in the late secretory (LS) phase from patients that developed sPE in a previous pregnancy (n=24) and controls without sPE (n=16). Controls include patients who had preterm birth with no signs of infection (n=8), and those at term labor with normal obstetric outcomes (n=8). The maternal and neonatal characteristics of the participants are summarized in Table 2. Table 2. Maternal and neonatal characteristics for endometrial donors

To rule out the existence of global transcriptomic differences based on gestational age at delivery an analysis was conducted comparing preterm and term control specimens. Principal component analysis (PCA) based on transcriptomic profile demonstrated absence of clustering of samples based on their gestational age (Figure 6A). Volcano plot showed zero differentially expressed genes (DEGs) between preterm and term control samples (Figure 6B). Together, these results point toward that the gestational age at delivery is an inert variable at the endometrial transcriptional level.

Our experimental design used a randomly split of samples into two cohorts, training (70%) and test (30%) set (Figure 1A). The random sampling occurs within each class (control and sPE), so the overall class distribution of the data was preserved. The training set (n=29) was used for biomarker identification to obtain a molecular fingerprinting encoding the defective decidualization in sPE, whereas the test set (n=l 1) was used for independent confirmation of our findings. All samples in both cohorts were processed and RNA-sequencing performed in the same manner.

GlobalRNAseq analysis was performed comparing gene expression paterns of sPE (n=17) and controls (n=12) from the training set. After quality trimming and filtering, the reads were aligned to the reference genome hgl9. The raw sequencing genes among the 29 samples were 56,638 and after normalization the number of genes included in the analysis was 18,301. Transcriptional analysis revealed 859 differential expressed genes (DEGs) between sPE and controls (FDR <0.05 and FC>2). Volcano plot showed the yellow dots denoting those statistically significant DEGs with at least two-fold (Figure IB). Specifically, a total of 262 up-regulated and 597 down-regulated genes were identified (Figure 1C). Table 3 is a complete list showing differentially expressed genes which were obtained in the Global RNAseq analysis (859 DEGs). FDR (False Discovery Rate), logFC (logarithim fold change), FC (fold change).

Table 3

Genes identified mostly include down-regulated mRNAs encoding genes involved in decidualization, such as MMP3, PRL , II -I /, IHH, and SGK1, genes associated to signaling (e.g. NR4A3 and IL8 ), response to growth factor (e.g. FGF1 and FGF14), angiogenesis (e.g. EDN2 and TMEM215) and immune response ( CCL20 , CXCL3, and IGHG1 ), among others. The up-regulated category identifies molecules involved in amino acid metabolic/catabolic processes ( GGT3P , 11)02, and PRODH ), transport and oxidoreductase activity. To validate dysregulated expression of specific genes identified in the global RNA-seq analysis, we assayed a subgroup of samples from the experimental cohort [controls (n=9) and sPE (n=14)]. Nine relevant genes were selected to validate their expression patterns in sPE vs. control groups (Figure 7A) by RT-qPCR. Fold changes were highly concordant with the sequencing data, corroborating our results (Figure 7B).

Example 2.2. In vivo vs in vitro decidualization fingerprinting

Previously, we reported a decidualization defect detected in endometrial stromal cells (hESCs) isolated from patients with a previous sPE as compared to patients with normal obstetric outcome, but this finding was restricted to the analysis of the endometrial stromal cells (hESCs) using an in vitro decidualization cell culture model. Now, we compared the overlapping between DEGs reported during in vitro (n=129) versus in vivo (n=859) decidualization in sPE compared with control patients. Eighteen genes differentially expressed between the two groups (FDR<0.05 and FC>2) overlapped in both approaches (Figure 2A). They include genes known to be involved during in vitro decidualization, some of them were up-regulated [e.g. ERP27, CHODL , and PRUNE2], and others down-regulated [e.g. ISM 1, MEST,MFAP2 , and REEP2 ]. The expression pattern of the common genes using a sequencing data is presented as a box plot using counts per million corroborating significant differential expression between sPE vs. control (Figure 2B). This result was confirmed by RT-qPCR (Figure 2C) and high correlation (R=0.99) was observed with the global RNAseq results (Figure 2D). However, our current in vivo approach confirms our previous in vitro finding and revealed a broad spectrum of deregulated transcripts.

Example 2.3. Identification of molecular fingerprinting encoding in vivo decidualization defect in sPE patients

To formulate the transcriptomic signature that encodes the in vivo defective decidualization (DD) detected in sPE, we selected those genes with significant deregulation (FDR<0.05), at least four-fold increase (FC> 4) between control and sPE with assigned EntrezID. Volcano plot showed a total of 166 DEGs meeting these criteria and were included in the final DD- fmgerprinting (Figure 3A). Table 4 is a complete list of genes selected as fingerprinting of defective decidualization (166 DEGs). FDR (False Discovery Rate), logFC (logarithrm fold change), FC (fold change).

Table 4 Interestingly, the number of down-regulated genes was higher compared with the up-regulated genes in sPE vs controls. This observation points toward that in vivo decidualization defect found in sPE are mainly mediated by lack of expression of a subset of genes.

Example 2.4.Functional analysis revealed numerous pathways deregulated in sPE

Gene ontology (GO) analysis of the gene signature associated to defective decidualization in sPE was performed identifying 479 enriched biological process (P -value < 0.005). The down- regulated biological processes highlighted were pathways associated mainly with basic functions such as immune response, signaling, extracellular matrix, cell motility, blood pressure regulation and reproductive process (Figure 3B). All of them are hallmarks of improper decidualization and sPE pathogenesis. More deeply, we found fingerprinting genes representative of the altered pathways in sPE such as, IL6 and TNF regulating the acute inflammatory response, MMP3 and MMP1 participating in the extracellular matrix disassembly, POSTN and REN as affected regulators of the systemic arterial blood pressure and PRL, IHH and ICAM1 implicated in the down-regulated response to hormone (Figure 3C). Up-regulated biological processes were associated with metabolism process, nervous system, interaction between organisms, and negative regulation of chorionic trophoblast cell proliferation. Functional analysis evidenced that the 166 DEGs between sPE vs. control pregnancies included in the fingerprinting were implicated in pathways related with decidualization corroborating the maternal contribution to sPE.

Example 2.5. Validation of the DD-fingerprinting to segregate sPE and control samples

Based on the 166 genes included in the DD-fingerprinting, principal component analysis (PCA) revealed that sPE and control samples clustered separately in two groups, except for three control samples (C20, C21 and C22) (Figure 4A). High variance between both groups was effectively captured in the first 2 principal components. Unsupervised hierarchical clustering analysis confirmed that the gene fingerprinting effectively segregated the two groups; one was composed mainly by controls and the other by sPE samples (Figure 4B). The same three controls were found clustered with the sPE group recreating the PCA results.

We next sought to validate the DD gene signature in an independent cohort of samples [sPE (n=7) vs. control (n=4)] to confirm our findings. Principal component analysis based on these transcripts effectively segregated samples in two homogeneous groups (Figure 4C) that was corroborated by hierarchical clustering (Figure 4D). These genes successfully grouped 100% of controls and 85.7% of sPE cases. Our results support the relevance of the DD-fmgerprinting as potential preconceptional biomarker of sPE.

Example 2.6. Down expression of progesterone receptor is highly connected with DD- fingerprinting genes in sPE

Progesterone, acting through its receptor (PR), is the key hormone modulating the decidualization. Interestingly, the fingerprinting that undergoes the defective decidualization phenotype of patients with a previous sPE, includes 106 enriched genes in the endometrium tissue defined by the human protein atlas and 50% of them were associated with PR (or PR binding site within EPR, 10 kb or 25 kb)(Figure 5A). Based on this interesting overlapping, we analysed the gene expression of PR in endometrial tissue from a subset of patients with previous sPE (N=13) and control (N=9) pregnancies using quantitative-polymerase chain reaction (Figure 5B). We found significant down-regulation of PR in sPE patients (P -value < 0.001) compared with controls. In order to investigate which PR isoform are responsible of this down-regulation, we determine the expression levels of PR-A and PR-B by quantitative- polymerase chain reaction (Figure 5C). The analysis revealed that PR-B was significantly down-regulated in sPE vs controls (P -value < 0.01), while PR-A was not deregulated (P -value > 0.05). This result corroborates that down-regulation of PR, mediated by altered expression of the isoform PR-B, is associated with sPE.

Functional analysis of the 53 genes from our DD fingerprinting that are modulated by the PR revealed many biological processes including categories related with decidualization biology as cell communication, cell-cell signaling, response to external stimulus, signal transduction and cell movement, leukocyte homeostasis, among others (Figure 5D). This result supports the close relationship of those genes with a proper decidualization through PR signalling.

Based on these robust results that correlates our DD fingerprinting with PR, we postulated that in a non-sPE pregnancy, progesterone (P) activates its receptor (PR) and modulates the action of Indian hedgehog (IHH) in the epithelium that induces the activation of PR in the stroma compartment. The signal transduction leads to proper decidualize the human endometrial stromal cells (hESC), which in turn interact with immune system and endothelial cells to prepare the tissue microenvironment to be successfully invaded by the cytotrophoblast cells (Figure 5E). In contrast, in sPE pregnancies, IHH expression was found downregulated, which affected the PR signalling pathway in the stromal compartment compromising decidualization, and consequently, affecting processes involved in immune system deregulation and endothelial dysfunction (Figure 5F). In detail, there were evidences of an impaired response to stimulus [e.g. IL8, CCL20 , and CNR I] and an epithelial-stromal crosstalk through IHH and FGF1, which could lead to an imbalance of hESC proliferation and differentiation. We found downregulated genes involved in those process, including PRL and WNT regulators and effectors —such as FGF1, FJX1, SFRP1, NKD1— and extracellular matrix degradation [e.g. MMP1, MMP9 and MMP11\. Other genes affected in sPE are related with AKT, which play an important role in cell proliferation and cell motility. Interestingly, ART downregulates PR-B and it has been described this modulation regulates angiogenesis. According to this, we found some affected genes associated with endothelial dysfunction through AX A , KLK3, EDN2 and PTGS2 mRNA alteration. Also, endothelial inflammation and oxidative stress likely be affected, since we found downregulated genes such as ICAM1, SELE and RBP7. Finally, immune system was prominently deregulated showing a high number of DEGs. Some examples were TN1 IL6, CXCL3 and IGHG1. Taken together, our findings reveal that the decidualization defect observed in patients with previous sPE might be derived by a deregulated progesterone signalling.

Example 2.7. Assays for assessing the proper time for collecting the samples.

The ERA test evaluates endometrial receptivity detecting the optimal day for the embryo transfer that is specific for each women. The protocol indicates to take an endometrial biopsy during the window of implantation (WOI), in a natural cycle correspond to 19-21 days of the menstrual cycle and in a hormone replacement therapy (HRT) cycle after five days of progesterone administration (120 hours from the first progesterone intake; P+5).

Our work demonstrates a decidualization transcriptional defect in vivo of endometrial biopsies obtained in the late secretory phase (1 to 6 days before menstruation started) from patients that developed severe preeclampsia (sPE) in a previous pregnancy compared with controls without sPE.

Thus, our aim was to investigate if this endometrial transcriptomic defect associated with preeclampsia could be detected at time of ERA samples collection (8 to 10 days before menstruation started). To test this objective, we performed a global transcriptomic profiling of endometrial tissue (ERA samples) from patients in whom severe sPE developed compared with a control pregnancies.

For this purpose, we classified endometrial biopsies taken for the ERA test depending of the pregnancy obstetric clinical information in control (healthy pregnancies) or preeclampsia.

This was the number of total samples:

• Control (N= 43).

• Preeclampsia (N= 46).

Based on the ERA diagnostic these samples were classified in:

• Pre-receptive day 1 (PREdl).

• Early receptive (ER).

• Receptive (R).

• Late receptive (LR).

• Post-receptive (POST).

The distribution of samples from women with a control or severe preeclampsia (sPE) pregnancy based on the ERA result is shown in Table 5.

Table 5

Global RNAseq analysis was performed using the ERA sample set of control (N=43) and sPE (N=46). The raw sequencing genes among the 89 samples were 56,638 and after normalization the number of genes included in the analysis was 19,941.

A principal component analysis (PCA) was plotted using the normalized gene expression, where each dot represents one sample. The dots were coloured based on different classification.

At Figure 8, the dots were coloured based on the ERA prediction (diagnostic): PREdl, eR, R, Late-R and POST. This analysis showed that samples are successfully grouped based on its ERA diagnostic, so the sample classification is based on their receptivity status. Figure 8 showed how the sample clustering is based on their colour:

Next, Figure 9 showed the dots colour based of the disease status: control or preeclampsia. While, we can see at the Figure 9, that the disease is not an important factor for grouping samples. They are mixed together.

In conclusion, transcriptomic profile of endometrial samples collected when ERA test is performed (19-21 day of menstrual cycle) failed to successfully cluster the control and preeclampsia samples. In conclusion, our work demonstrates that the samples should be collected during late secretory menstrual cycle (1 to 6 before menstrual cycle ends) to detect transcriptional differences between control and preeclampsia that let successful classify the samples in the two groups.

Example 2.8. Selection of differentially expressed (DE) genes

A gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.

A gene deregulation could be responsible of the physiological differences associated with severe preeclampsia.

We performed an RNA sequencing to obtain the gene expression profiling of each sample included in our study. The data analysis was focused on pairwise comparisons between the groups control vs severe preeclampsia (sPE), revealing the DE genes between the two groups. So, we found a high number of genes that were significantly deregulated in sPE compared with control specimens.

The parameters used to consider a gene as DE were:

Adjusted p-value with a cut-off of 0.05. The adjustment method was “FDR”. • Level of deregulation (sPE vs control) is referred as log2 -fold-change (logFC). We used the following cut-offs: |1|, |2|, |2.5|, and |3|. In detail, genes with expression > 1 were considered upregulated, while genes with expression < -1 were downregulated. The same principle for the rest of cut-offs.

Table 6 shows the number of DE genes obtained for each cut-off of log2FC.

Table 6

The DE genes were used to plot the dendrogram and principal component analysis (PCA). These plots allow us to observe the similarity or dissimilarity among samples based on the DE genes. The expected result was to obtain two separated groups (sPE and control). That distribution of samples would mean that the DE genes enable to classify samples in sPE or control.

In all cases, the result obtained was that DE genes selected by different fold change cut-off (1, 2, 2.5 and 3) segregate successfully the sPE and control samples. In all cases, only three controls were misclustered.

So, according to the log2FC, the genes were classified as shown in the following tables: Table 3 (859 genes) (log2FC >1), Table 8 (197 genes) (log2FC >2), Table 9 (90genes) (log2FC >2.5) and Table 10 (47 genes) (log2FC >3). Hgnc (Gene Nomenclature Committee), logFC (logarithim FC fold change), logCPM (logarithm counts per million), PValue (P-value) and FDR (False Discovery Rate).

Table 8

Table 9

Table 10

Example 3. Second analysis of the global transcriptional signature of defective decidualization in vivo from sPE patients 3.1. Endometrial transcriptome alterations during decidualization in sPE

To identify transcriptomic alterations during decidualization in sPE, we applied global RNA sequencing (RNA-seq) to endometrial biopsies obtained in the late secretory phase from women who developed sPE in a previous pregnancy (n = 24) and controls who never had sPE (n = 16) (GSE172381). Clinical maternal and neonatal characteristics of the participants are summarized in Table 11.

Table 11: Maternal and neonatal characteristics for endometrial donors.

After quality trimming and filtering, reads were aligned to the reference genome hgl9. The 40 samples produced 56,638 raw sequencing genes; after normalization, 18,301 genes were included in the analysis. Biological and technical variables for each donor were considered to discard confounding effects on the transcriptomic profile. Controls included women who had a preterm birth with no signs of infection (n = 8) and women who gave birth at full term with normal obstetric outcomes (n = 8). Transcriptomic profiles were compared by differential expression analysis, revealing no significant changes in the endometrial transcriptome between preterm and term controls (FDR > 0.05; Figure 10). Principal component analysis (PCA) supported that there was no underlying pattern of distribution depending on gestational age at delivery (Figure 10). Once we ruled out bias on controls, we randomly split samples into two cohorts, a training set (70%) and a test set (30%) (Figure 10A). Random sampling occurred within each class (sPE and controls), so overall class distribution of the data was preserved. The training set (n = 29) was used for the identification of molecular fingerprinting encoding DD in sPE, while the test set (n = 11) was used to confirm our findings. All samples in both cohorts were processed and sequenced in the same manner.

Transcriptional analysis in the training set was performed by comparing gene expression patterns in sPE (n = 17) and controls (n = 12). This comparison revealed 593 DEGs based on FDR < 0.05 and with at least 1.2 FC between groups (FC > 1.2). DEGs are shown in the volcano plot through yellow dots (Figure 10B). A total of 155 upregulated and 438 downregulated DEGs were identified as being associated with DD in sPE (Figure IOC; complete list in Table 12)).

Table 12: The 593 statistically differential expressed genes (FDR < 0.05) with at least 1.2-fold change (FC > 1.2) in sPE vs control cases obtained from RNA-seq analysis.

Downregulated transcripts include those involved in decidualization, such as MMP3, PRL, IL- 6, and IHH; and genes associated with signaling (e.g., NR4A3 and IL8), growth factors (e.g., FGF1 and FGF7), angiogenesis (e.g., EDN2 and TMEM215), and immune response (CCL20, CXCL3, and IGHG1). Upregulated genes are involved in amino acid metabolic/catabolic processes (ID02 and CAPN3), transport, and oxidore- ductase activity.

3.2. Comparison of DD transcriptomics in previous sPE in vivo vs. in vitro

We previously described DD in human endometrial stromal cells (hESCs) isolated from women with previous sPE compared to women with normal obstetric outcomes, but this finding was restricted to the stromal cell population using an in vitro decidualization cell culture model (Garrido-Gomez et al., 2017). Here, we compared DD overlapping between DEGs reported in vitro (n = 129) vs. in vivo (n = 593) in sPE compared to control women. Nine genes were overlapped between the two datasets (Figure 11A); one gene was upregulated (ERP27), and eight genes were downregulated (e.g., ISM1, MEST, MFAP2, and REEP2). The expression pattern of common genes is presented as a box plot using counts per million, corroborating significant differential expression between sPE and control (Figure 11B). Recently, in vivo transcriptomics of endometrium at single-cell resolution across the menstrual cycle were characterized (Wang et al., 2020). Transcriptome profiles of stromal fibroblasts from the late secretory phase allowed the identification of deregulated genes in sPE as associated to hESC. We found that 263 genes from the 593 DEGs in sPE vs. control are expressed by hESC (Figure 11C). Taken together, the in vivo assessment provides a broad spectrum of dysregulated transcripts comparing with previous in vitro findings, which includes a high concordance with in vivo hESC genes.

3.3. Identification of the fingerprint encoding human endometrial DD To formulate the transcriptomic signature that encodes DD detected in sPE in vivo, we selected genes with significant dysregulation (FDR < 0.05) and at least 1.4-fold increase (FC >1.4) between sPE and control with assigned EntrezID. A volcano plot shows 120 DEGs meeting these criteria included in the final DD signature (Figure 12A; complete list of genes is included in Table 13.

GO analysis of the gene signature associated with DD in sPE identified 151 enriched biological processes downregulated (FDR < 0.05). These pathways were associated with cell cycle, DNA damage response, cell signaling, cellular response, cell motility, extracellular matrix, immune response, and reproductive process (Figure 12B). All are hallmarks of impaired decidualization and sPE pathogenesis. We identified fingerprinting genes representative of the altered pathways in sPE, such as IL6 and TNF, regulating the response to bacterial molecules, MMP3 and MMP1 participating in the extracellular matrix organization, and TNF, IL8, and FGF1 implicated in the downregulated receptor signaling (Figure 12C). Functional analysis revealed that the 120 DEGs included in DD fingerprinting are implicated in pathways related to decidualization, corroborating the maternal contribution to sPE. Interestingly, the number of downregulated genes was higher than the number of upregulated genes in sPE compared to controls, suggesting that, in vivo, DD may be induced by the lack of expression of a subset of genes.

Based on the 120 genes included in the DD signature, PC A showed that sPE and control samples clustered separately in two groups, except for three control samples (C20, C21, and C22) (Figure 13A). High variance between groups was effectively captured in the first two principal components. Unsupervised hierarchical clustering analysis confirmed that gene fingerprinting effectively segregated the two groups: one encompassing mainly controls and the other mainly sPE samples (Figure 13B). The same three controls clustered with the sPE group, recreating the PC A results.

To validate the DD gene signature in an independent cohort of samples (sPE [n = 7] vs. control [n=4]), PCA based on these transcripts effectively segregated samples in two homogeneous groups (Figure 13C), corroborated by hierarchical clustering (Figure 13D). These genes successfully grouped 100% of controls and 85.7% of sPE cases supporting DD in sPE.

3.4. DD fingerprint in sPE is connected to ER1 and PR-B Of the 120 genes in the DD signature, 94 endometrial enriched genes encode for specific proteins reported by the Human Protein Atlas (Uhlen et al., 2015). Interestingly, 45 of those genes (47.9%) were included in the transcriptome modulated by ESR1 (Hewitt et al., 2010), and 43 genes (45.7%) overlapped with the transcriptome and cistrome associated with PGR (Mazur et al., 2015; Figure 14A). Regarding target genes of ER1 and PR, the database of Human Transcription Factor Targets (hTFtarget) reported 17 genes responsive to ER1 and 50 target genes modulated by PR, based on epigenomic, CHIP-seq, or motif evidence (Zhang et al., 2020).

We evaluated the interaction between steroid receptor signaling and the proteins encoded by DD fingerprinting genes in sPE by building a dynamic network including ER1 and PR. String software (Jensen et al., 2009) was used to construct network connections visualized with Cytoscape software (Shannon et al., 2003). The interactome contained 117 nodes directly interconnected by 361 edges (Figure 14B). This DD fingerprint network showed a highly enriched protein-protein interaction (PPI) in sPE; indeed, the interconnection between nodes was significantly higher than the 93 edges expected (PPI enrichment p<1.0e-16). Clustering revealed three main modules based on their connectivity degree, with functionally relevant genes involved in gland morphogenesis, cell migration, extracellular matrix organization, stromal cell differentiation, cellular response to DNA damage stimulus, and regulation of cell cycle. The hub genes were determined by overlapping the top 10 genes obtained using two topological analysis methods in the cytoHubba plugin (Chin et al., 2014), MCC, and MNC. Five genes were selected, all of which were downregulated. Interestingly, both ER1 and PR were strongly embedded in the network and highly connected with DD fingerprinting, highlighting the interaction of hormonal receptors with notable decidualization mediators such as IHH and MSX2 validated by RT-qPCR (Figure 14C and D). Furthermore, the interactome demonstrated a direct interaction between ER1 and PR. These results support the transcriptomic dysfunction of the genes present in the DD signature through imbalanced hormone receptor signaling in sPE.

We then analyzed the expression of ESR1 and PGR in the endometrial tissue from a subset of women with prior sPE (N = 13) compared to controls (N = 9) by RT-qPCR. We found reduced expression of transcripts encoding the hormone receptors ESR1 (p<0.05) and PGR (p<0.001) in sPE patients (Figure 14E and F). In-depth expression analyses revealed that the isoform PGR-B was significantly downregulated in sPE vs. controls (p<0.01), while the isoform PGR- A was unaffected (p>0.05) (Figure 14G and H). These results were confirmed at the protein level by immunohistochemical analysis of ER1 and PR-B in endometrial biopsies collected in the late secretory phase from women with previous sPE (n = 4) and controls (n = 4) (Figure 141 and J). Both receptors were highly expressed through the decidualized endometrium, especially in the secretory glands in controls. In contrast, their expression was greatly reduced or absent in sPE samples. These results suggest that the DD transcriptomic signature implicates dysregulated ER1 and PR-B signaling in the late secretory phase in sPE patients.

Example 3.5. Selection of differentially expressed (DE) genes

A gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.

A gene deregulation could be responsible of the physiological differences associated with severe preeclampsia.

We performed an RNA sequencing to obtain the gene expression profiling of each sample included in our study. The data analysis was focused on pairwise comparisons between the groups control vs severe preeclampsia (sPE), revealing the DE genes between the two groups. So, we found a high number of genes that were significantly deregulated in sPE compared with control specimens.

The parameters used to consider a gene as DE were:

• Adjusted p-value with a cut-off of 0.05. The adjustment method was “FDR”.

• Level of deregulation (sPE vs control) is referred as log2 -fold-change (logFC). We used the following cut-offs: |1|, |2|, |2.5|, and |3|. In detail, genes with expression > 1 were considered upregulated, while genes with expression < -1 were downregulated. The same principle for the rest of cut-offs.

Table 6 shows the number of DE genes obtained for each cut-off of log2FC.

Table 14

The DE genes were used to plot the dendrogram and principal component analysis (PCA). These plots allow us to observe the similarity or dissimilarity among samples based on the DE genes. The expected result was to obtain two separated groups (sPE and control). That distribution of samples would mean that the DE genes enable to classify samples in sPE or control.

In all cases, the result obtained was that DE genes selected by different fold change cut-off (1, 2, 2.5 and 3) segregate successfully the sPE and control samples. In all cases, only three controls were misclustered. So, according to the log2FC, the genes were classified as shown in the following tables: Table 15 (445 genes) (log2FC>l), Table 16 (135 genes) (log ₂ FC>2), Table 17 (71 genes) (log2FC > 2.5) and Table 18 (42 genes) (log2FC > 3). Hgnc (Gene Nomenclature Committee), log2FC > (logarithm FC fold change), logCPM (logarithm counts per million), PValue (P-value) and FDR (False Discovery Rate).

Table 15

Table 16

Table 17

Table 18

Example 3.6. Selection of differentially expressed (DE) genes identified in the second analysis but not identified in the first analysis A gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.

We performed an RNA sequencing to obtain the gene expression profiling of each sample included in our study. The data analysis was focused on pairwise comparisons between the groups control vs severe preeclampsia (sPE), revealing the DE genes between the two groups. So, we found a high number of genes that were significantly deregulated in sPE compared with control specimens.

The parameters used to consider a gene as DE were: · Adjusted p-value with a cut-off of 0.05. The adjustment method was “FDR”.

• Level of deregulation (sPE vs control) is referred as log2 -fold-change (logFC). We used the following cut-offs: |1|, |2|, |2.5|, and |3|. In detail, genes with expression > 1 were considered upregulated, while genes with expression < -1 were downregulated. The same principle for the rest of cut-offs. Table 20 shows the total number of DE genes that were identified in the second analysis but not in the first analysis (150 genes)

Table 20

Table 21 shows the DE genes forming part of the defective decidualization fingerprinting that were identified in the second analysis but not in the second analysis (47 genes) Table 21 (ίoik' S mbol PViiliio I (

CDH24 64403 5,04321E-05 0,019683522 -1,4577474 -3

CDT1 81620 3,21621E-05 0,016856612 -1,8414058 -4

CENPH 64946 9,32773E-05 0,025983952 -1,3559079 -3

DEPDC1 55635 0,000360157 0,047190803 -1,9870333 -4

DERL3 91319 4,39346E-05 0,019188979 -1,7165019 -3

E2F1 1869 0,000293676 0,044157354 -1,6219685 -3

ENC1 8507 6,62848E-05 0,023383252 -1,7837816 -3

FBN2 2201 0,00023381 0,040847767 -1,8648233 -4

FOLH1 2346 0,000263435 0,042764987 -1,2528085 -2

GGH 8836 0,000249052 0,041734633 -1,327446 -3

GINS2 51659 7,45766E-05 0,024000579 -1,7662281 -3

ITIH5 80760 0,000190369 0,037549851 -1,4824169 -3

KCNN1 3780 0,000225184 0,040497789 -1,9915349 -4

LAMA1 284217 0,000108317 0,028091059 -1,9422547 -4

LPIN3 64900 0,000270354 0,043337549 1,39360305 3

MAD2L1 4085 0,000310262 0,045245377 -1,4938115 -3

MDK 4192 0,000108259 0,028091059 -1,3204592 -2

MND1 84057 1,01168E-05 0,011598949 -1,9364169 -4

NAT8L 339983 0,000390149 0,049923981 -1,4223337 -3

NREP 9315 0,000231216 0,040782863 -1,8059789 -3

ORAOV1P1 100873907 0,000346511 0,047035434 2,00020989 4

PAG1 55824 0,000123285 0,029421714 -1,6461354 -3

POC1A 25886 0,000182385 0,036765574 -1,2783295 -2

PODXL2 50512 5,40379E-05 0,0206515 -1,6230071 -3

PRKXP1 441733 7,34087E-05 0,024000579 1,81295237 4

PTTG1 9232 0,000132644 0,030660126 -1,7185713 -3

REEP2 51308 0,000390239 0,049923981 -1,6323242 -3

RIMBP2 23504 0,000288534 0,044157354 1,7214548 3

RIMS2 9699 0,000213307 0,040045836 1,9991805 4

RNASEH2A 10535 0,000127691 0,030030396 -1,1237793 -2

SKA 3 221150 0,000340667 0,047035434 -1,9264692 -4

TIPIN 54962 0,000179616 0,036609649 -1,0144111 -2

TK1 7083 3,06521E-05 0,016537734 -1,7629718 -3

UBE2T 29089 0,000121428 0,029421714 -1,5133385 -3

UHRF1 29128 4,54212E-05 0,019350768 -1,8413797 -4

XRCC2 7516 0,000348988 0,047035434 -1,6481011 -3

ZNF367 195828 0,000141751 0,03127971 -1,7971907 -3

*FC: indicates downregulated genes calculated as -POWER(2,-logFC)

Further aspects of the invention

1. In vitro method for the diagnosis and/or prognosis of preeclampsia which comprises: a. Measuring the expression level of at least a gene selected from Table 3, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In vitro method, according to aspect 1, which comprises: a. Measuring the expression level of at least a gene selected from Table 4, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In vitro method, according to any of the previous aspects, which comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2fold change of at least 1 for the expression of at least a gene included in Table 3, a log2fold change of at least 2 for the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 for the expression of at least a gene included in Table 9 or a log2 fold change of at least 3 for the expression of at least a gene included in Table 10 In vitro method, according to any of the previous aspects, which comprises: a. Measuring the expression level of all genes comprised in Table 10, or in Table 9, or in Table 8, or in Table 3, or in Table 4, or in Table 3, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein a deviation in the level of expression of the genes measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In vitro method, according to any of the previous aspects, which comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2fold change of at least 1 for the expression of all genes included in Table 3, a log2fold change of at least 2 for the expression all genes included in Table 8, a log2 fold change of at least 2.5 for the expression all genes included in Table 9 or a log2 fold change of at least 3 for the expression of all genes included in Table 10.

6. In vitro method, according to any of the previous aspects, which comprises: a. Measuring the expression level of progesterone receptor in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein the determination of a lower the level of expression in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.

7. In vitro method, according to any of the previous aspects, characterized in that it is a computer implemented method, which comprises: a. Entering into the computer the level of expression of the genes obtained from healthy control subjects; b. Entering into the computer the level of expression of the genes obtained in the step a) of the previous claims; c. Producing a score which is displayed on the device; d. Determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with a pre-established threshold level of expression determined in healthy control subjects.

8. In vitro method, according to any of the previous claims, wherein the biological sample is an endometrial tissue sample.

9. In vitro use of at least a gene selected from Table 3 for the diagnosis and/or prognosis of preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.

10. In vitro use, according to aspect 9, of at least a gene selected from Table 4.

11. In vitro use, according to any of the aspects 8 to 10, of the progesterone receptor.

12. In vitro use, according to any of the aspects 8 to 11, wherein the biological sample is an endometrial tissue sample.

13. Kit for implementing any of the methods according to aspects 1 to 8 which comprises: a. Reagents for measuring the level of expression of at least a gene selected from Table 3, or any combination thereof, b. Tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle. 14. Kit, according to aspect 13, which comprises: a. Reagents for measuring the level of expression of at least a gene selected from Table 4, or any combination thereof, b. Tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle. 15. Use of the kit according to aspects 13 or 14, for the diagnosis and/or prognosis of preeclampsia.