WO2020212588A1

WO2020212588A1 - Non-invasive assay for pre-eclampsia and conditions associated with pre-eclampsia

Info

Publication number: WO2020212588A1
Application number: PCT/EP2020/060891
Authority: WO
Inventors: Carlos SIMÓN VALLÉS; Juan Antonio DÍEZ; Tamara GARRIDO
Original assignee: Igenomix, S.L.
Priority date: 2019-04-17
Filing date: 2020-04-17
Publication date: 2020-10-22

Abstract

The present invention provides methods for evaluating, diagnosing and/or predicting the occurrence of one or more systemic conditions associated with pre-eclampsia. The present invention further provides systems, materials, and methylation markers for performing said methods. The present invention further relates to methods of treating a systemic condition associated with pre-eclampsia, or managing a heathy lifestyle that mitigates against a detected risk of developing such pre-eclampsia associated conditions later in life. Further provided are methods for detecting a risk of developing a systemic condition associated with pre-eclampsia, comprising: (i) isolating cell-free DNA from a pre-eclampsia patient sample; (ii) analyzing the cell-free DNA for the presence of one or more methylation markers appearing on the cell-free DNA, wherein the one or more methylation markers are predictive of a condition associated with pre-eclampsia.

Description

NON-INVASIVE ASSAY FOR PRE-ECLAMPSIA AND CONDITIONS ASSOCIATED

WITH PRE-ECLAMPSIA

FIELD OF THE INVENTION

[0001] The present invention relates to a method for evaluating, diagnosing and/or predicting the occurrence of one or more systemic conditions associated with pre-eclampsia. The present invention further relates to systems, materials, and methylation markers for performing said methods. The present invention further relates to methods of treating a systemic condition associated with pre-eclampsia once identified.

BACKGROUND OF THE INVENTION

[0002] Pre-eclampsia is a syndrome of hypertension, edema, and proteinuria that affects 5% to 10% of pregnancies and results in substantial maternal and fetal morbidity and mortality. Pre eclampsia accounts for at least 200,000 maternal deaths worldwide per year. The symptoms of pre-eclampsia typically appear after the 20th week of pregnancy and are usually detected by routine monitoring of blood pressure and urine. Pre-eclampsia can vary in severity from mild to life threatening. A mild form of pre-eclampsia can be treated with bed rest and frequent monitoring. For moderate to severe cases, hospitalization is recommended and blood pressure medication or anticonvulsant medications to prevent seizures are prescribed. If the condition becomes life threatening to the mother or the baby the pregnancy is terminated and the baby is delivered pre-term. If undiagnosed, preeclampsia can lead to eclampsia, a serious condition that can put the pregnant woman and her baby at risk, and in rare cases, cause death. Delivery of the pregnancy continues to be the only effective treatment.

[0003] While the clinical symptoms of pre-eclampsia can be resolved after delivery of the placenta, pre-eclampsia continues to affect a women’s health over her lifetime. Specifically, pre-eclampsia increases a woman’s risk for subsequent cardiovascular complications, such as heart disease, stroke, and venous thromboembolism, over her lifetime after delivery (11). In addition, these women have an increased risk of dying from cerebrovascular disease after pre eclampsia compared to women who had a healthy pregnancy. Still further, pre-eclampsia women having an increased risk of chronic kidney disease later in life (12).

[0004] Unfortunately, there are currently no reliable techniques for determining or predicting whether a subject having pre-eclampsia will develop a cardiovascular and/or chronic kidney disease or other pre-eclampsia-related disorder later in life. Such techniques would substantially advance women’s health.

SUMMARY OF THE INVENTION

[0005] This specification describes and enables methods for evaluating, diagnosing and/or predicting the occurrence of one or more systemic conditions associated with pre-eclampsia. Aspects of the technology are based, at least in part, on the discovery by the inventors that the presence, in maternal blood of a patient having pre-eclampsia, of methylated cell-free DNA derived from a maternal tissue or organ (as opposed to fetal tissue or placental tissue) can be indicative of long term damage to that tissue or organ. Thus, in one aspect, the present invention relates to examining, detecting, or otherwise evaluating a sample of cell-free DNA for the presence of one or more methylation markers (e.g., differentially methylated regions or DMRs) that are indicative of a pre-eclampsia-related condition, e.g., a cardiovascular or kidney disorder.

[0006] The present invention further provides systems, materials, and methylation markers for performing said methods. The present invention further relates to methods of treating a condition associated with pre-eclampsia, or managing a heathy lifestyle that mitigates against the risk of developing such a pre-eclampsia associated conditions later in life. Further provided are methods for detecting a risk of developing a systemic condition associated with pre eclampsia, comprising: (i) isolating cell-free DNA from a pre-eclampsia patient sample; (ii) analyzing the cell-free DNA for the presence of one or more methylation markers methylation markersappearing on the cell-free DNA. In certain embodiments, the methylation markers are disease-associate methylation markers that are indicative of a condition associated with pre eclampsia (e.g., an existing condition or an increased risk of developing a condition that is associated with pre-eclampsia).

[0007] In some embodiments, the cell-free DNA is cell-free methylated DNA that is present in a biological sample obtained from the subject. Accordingly, in some embodiments, the biological sample is processed to isolate total cell-free DNA present in the sample and then processed to isolate the methylated cell-free DNA. In other embodiments, the cfDNA is maternal in origin.

[0008] In some embodiments, a biological sample is obtained from a subject prior to pregnancy or early in pregnancy (e.g., before the subject develops pre-eclampsia). In some embodiments, total cell-free DNA and/or methylated cell-free DNA is isolated from the biological sample and stored to be used as a reference. In various embodiments, the samples are maternal in origin. [0009] In various embodiments, the samples are from blood or plasma. In one embodiment, the samples are from blood. In another embodiment, the samples are from plasma. In still other embodiments, the samples are from serum.

[0010] In various aspects, the present invention relates to a method for detecting an elevated risk of developing a systemic condition associated with pre-eclampsia, comprising: (i) isolating cell- free DNA from a pre-eclampsia patient sample; (ii) analyzing the cell-free DNA for the presence of one or more methylation markers appearing on the cell-free DNA, wherein the one or more methylation markers are predictive of a condition or disease associated with pre-eclampsia, or a disease or condition that could develop later in life that is a result of pre-eclampsia earlier in life.

[0011] In certain cases, the pre-eclampsia associated condition that is being evaluated is a cardiovascular condition, which can include hypertension, chronic kidney failure,

glomerulosclerosis, acute kidney injury, glomerulonephritis, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia. In various embodiments, the one or more methylation markers occurs or annotes in a gene, or a promoter of a gene, or a transcription factor binding site of a gene associated with hypertension, chronic kidney failure,

glomerulosclerosis, acute kidney injury, glomerulonephritis, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia.

[0012] In other cases, the pre-eclampsia associated condition is a kidney condition, such as chronic kidney disease, acute kidney injury, glomerulosclerosis, and glomerulonephritis. In various embodiments, the one or more methylation markers occurs or annotes in a gene, or a promoter of a gene, or a transcription factor binding site of a gene associated with chronic kidney disease, acute kidney injury, glomerulosclerosis, or glomerulonephritis.

[0013] In various embodiments, the step of isolating cell-free DNA comprises methyl-CpG- binding domain-based (MBD) capture.

[0014] In various embodiments, the one or more methylation marker are predictive of a cardiovascular condition, which can include hypertension, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia. In various embodiments, the one or more methylation markers occurs or annotes in a gene, or a promoter of a gene, or a transcription factor binding site of a gene associated with hypertension, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia.

[0015] In other embodiments, the one or more methylation marker are predictive of a kidney condition, such as reduced glomerular filtration rate or kidney fibrosis. In various embodiments, the one or more methylation markers occurs or annotes in a gene, or a promoter of a gene, or a transcription factor binding site of a gene associated with kidney condition, such as reduced glomerular filtration rate or kidney fibrosis.

[0016] In various embodiments, the one or more methylation markers are indicative of a pre- eclampsia-related condition, e.g., a cardiovascular or kidney disorder. In certain embodiments, the methylation markers can occur or annotate in the promotor of a gene, e.g., a promoter of a gene, or a transcription factor binding site of a gene associated with cardiovascular health or kidney health.

[0017] Without being bound by theory, methylation of nucleobases in the promoter of a gene can affect the transcription of that gene. In general, without being bound by theory, increased methylation (hypermethylation) in a promoter of a gene correlates with lower transcription of that gene. Conversely, lower methylation (hypomethylation) in a promoter of a gene correlates with increase transcription of that gene.

[0018] In various embodiments, the methylation marker can be a differentially methylated region (DMR) that is a hypermethylated region. The hypermethylated region can, in some cases, be annoted to a promoter of a gene associated with a disorder. In other cases, the hypermethylated region can be annotated to a transcription factor binding site of a gene associated with a disorder.

[0019] In other embodiments, the methylation marker can be a differentially methylated region (DMR) that is a hypomethylated region. The hypomethylated region can, in some cases, be annoted to a promoter of a gene associated with a disorder. In other cases, the hypomethylated region can be annotated to a transcription factor binding site of a gene associated with a disorder.

[0020] In other embodiments, the one or more methylation markers is a DMR in a gene involved in pre-eclampsia, type 2 diabetes, chronic renal failure, hypertension, liver metabolic dysfunction, HDL cholesterol levels, LDL cholesterol levels, metabolic syndrome, or chronic kidney disease.

[0021] In various embodiments, the methylation markers have a fold change in methylation frequency of at least 1.1, or at least 1.2, or at least 1.3, or at least 1.4, or at least 1.5, or at least 1.6, or at least 1.7, or at least 1.8, or at least 1.9, or at least 2.0, or at least 2.2, or at least 2.4, or at least 2.6, or at least 2.8, or at least 3.0, or at least 3.5, or at least 4.0, or at least 5.0, or at least 6.0, or at least 7.0, or at least 8.0, or at least 9.0, or at least 10.0-fold greater than in a comparison control tissue that does not have the disease. [0022] In various other embodiments, the methylation markers have a fold change in methylation frequency of at least 1.1, or at least 1.2, or at least 1.3, or at least 1.4, or at least 1.5, or at least 1.6, or at least 1.7, or at least 1.8, or at least 1.9, or at least 2.0, or at least 2.2, or at least 2.4, or at least 2.6, or at least 2.8, or at least 3.0, or at least 3.5, or at least 4.0, or at least 5.0, or at least 6.0, or at least 7.0, or at least 8.0, or at least 9.0, or at least 10.0-fold lower than in a comparison control tissue that does not have the disease.

[0023] In various embodiments, the one or more methylation markers is annoted to a gene from the renin-angiotensin-aldosterone system (RAAS), such as“STK40” “STK39” ΈNRER” “CYP11B2”“SCNN1B” “NEDD4L” “SCNN1D” “NOS3” “PRCP” “SCNN1A” and “ACE”,“ENPEP” “CYP11B2”“SCNN1B” “NEDD4L” “SCNN1D” “NOS3” “PRCP” “SCNN1A” “ACE”. In various other embodiments, the one or more methylation markers is annoted to a promoter of a gene from the renin-angiotensin-aldosterone system (RAAS), such as “STK40” “STK39” ΈNRER” “CYP11B2”“SCNN1B” “NEDD4L” “SCNN1D” “NOS3” “PRCP” “SCNN1A” and“ACE”,“ENPEP” “CYP11B2”“SCNN1B” “NEDD4L” “SCNN1D” “NOS3” “PRCP” “SCNN1 A” “ACE”. In still other embodiments, the one or more methylation markers is annoted to a transcription factor binding site of a gene from the renin-angiotensin-aldosterone system (RAAS), such as“STK40” “STK39” “ENPEP” “CYP11B2”“SCNN1B” “NEDD4L” “SCNN1D” “NOS3” “PRCP” “SCNN1A” and “ACE”,“ENPEP” “CYP11B2”“SCNN1B” “NEDD4L” “SCNN1D” “NOS3” “PRCP” “SCNN1A” “ACE”.

[0024] In various other embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene implicated in immune regulation. Such genes can include, for example, IRF4, HLA-A, HLA-D, and TYK2. In one embodiment, the methylation marker occurs in the promoter region of the indicated genes.

[0025] In various other embodiments, the one or more methylation markers is implicated in blood circulation and annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene selected from the group consisting of: CD 28, NKX2.5, ASIC 2, CAMK2D, GAS6, GUCY1B3, and PDE4D. In one embodiment, the methylation marker occurs in the promoter region of the indicated genes.

[0026] In still other embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene selected from the group consisting of: NRXN1, CAMK2D, EPHA2, KIF5C, ASIC2, CRHBP, KCNA1, SPOCK1, ITGA8, SLC5A7, CYNC1H1, SHANK1, CD302, OLFM1, MAP IS, PDE1C, ALCAM, DZIP1, RGMA, and MLPH. In one embodiment, the methylation marker occurs in the promoter region of the indicated genes.

[0027] In still further embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene implicated in endometrial decidualization and meterno-fetal interactions, including ALCAM and GDF10.

[0028] In still further embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene implicated in trophoblast invasion and differentiation, including TLX1, HOXA9, CD38, LPL and NKX2.5.

[0029] In still other embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene selected from the group consisting of: ENaC, CYP11B2, STK9, NEDD4L,NOS3, or SCNN1D, and predictive of high blood pressure.

[0030] In other embodiments, the one or more methylation markers is a DMR occurring in a transcription factor binding site (TFBS) of a gene. Exemplary DMRs annotate to various TFBS sequences including, but are not limited to, HDAC1, SIN3A, CTCFL, SUZ12, EXH2, BABPA, TAF7, TCF3, EGR1, NRF1, ZBTB7A, HMBN3, CHD1, SAP 30, CTBP2, CCN72, E2F6, SP4, HDAC8, RBBPS, UBTF, PHF8, and KDM5B.

[0031] In other embodiments, the one or more methylation markers is a DMR occurring in a transcription factor binding site (TFBS) of a promoter of a gene. Exemplary DMRs annotate to various TFBS sequences including, but are not limited to, GATZ3, MEF2C, JUN, SP11, STAT3, CEB28, IRF4, FOS, BATF, GATA2, MAFK, ATF1, ARID3A, BCL11A, P05F1, FOXA2, TALI, PROM1, IRF3, ZNF217, FOXA1, MAFF, NFE2, and FAM48A.

[0032] In some embodiments, the one or more methylation markers is a DMR associated with gene pathways involved in the maternal-fetal interaction. In one aspect, the DMRs are associated with the following genes involved in cell projection: NRXNl, CAMK2D, EPHA2, KIF5C,

ASIC2, CRHBP, KCNA1, SPOCK1, ITGA8, SLC5A 7, DYNC1H1, SHANK1, CD302, OLFM1, MAP IS, PDE1C, ALCAM, DZIP1, RGMA, and MLPH. In one embodiment, the DMRs are associated with the following genes involved in regulation of cell differentiation: SEMA6D, PLXNA4, NKX2-5, OLFM1, OBSL1, SHANK1, NRG3, and GAS6. In another aspect, the DMRs are associated with the following genes involved in vascular homeostasis: CD38, NKX2.5, ASIC2, CAMK2D, CAS6, GUYCY1B3, and PDE4D. In other embodiments, the DMRs are associated with the following genes involved in cell communication: NKX2.5, VWC2, EPHA2, FOXP1, TBX18, and CELF4. In still other embodiments, the DMRs are associated with the following genes involved in immune regulation: IRF4, HLA-A, HLA-B, HLA-D, ΊΎK2, CAM2D, TCF3, and PRDM16. In still other embodiments, the DMRs are associated with the following genes involved in decidua function: ALCAM, GDF10, TLX1, HOXA9, CD38, and NKX2-5.

[0033] In some embodiments, the one or more methylation markers is a DMR associated with a gene of Table 1 relating to hypertension, chronic kidney failure, glomerulosclerosis, acute kidney injury, or glomerulonephritis.

[0034] In other embodiments, the methods disclose herein involve methylation markers of Table 2. Table 2 lists DMRs that annotate to genes of Table 1 (i.e., those genes which relate to hypertension, chronic kidney failure, glomerulosclerosis, acute kidney injury, or

glomerulonephritis) .

[0035] In other embodiments, the methods involve methylation markers of Table 3. Table 3 lists DMRs that annotate to genes exclusive to the group. That is, the DMRs annotate either as hypermethylated regions in the Control group (non-bolded text) or as hypermethylated regions in sPE samples (bolded text).

[0036] Table 3 - DMRs which annotate either as hypermethylated regions in the control group (non-bolded text) or as hypermethylated regions in sPE samples (bolded text).

[0037] In other embodiments, the methods involve methylation markers of Table 4. Table 4 lists DMRs that annotate to genes of the renin-angiotensin-aldosterone system (RAAS). That is, the DMRs annotate either as hypermethylated regions in the control group (non-bolded text) or as hypermethylated regions in sPE samples (bolded text).

[0038] In other embodiments, the methods involve methylation markers of Table 5. Table 5 lists DMRs that annotate to transcription factor binding sites (“TF”) in the PRCP gene.

[0039] In other embodiments, the methods involve methylation markers of Table 6. Table 6 lists DMRs that annotate to transcription factor binding sites (“TF”) in the ACE gene.

[0040] In various embodiments, the step of analyzing the cell-free DNA for the presence of one or more methylation markers comprises sequencing the cell-free DNA, e.g., by low-pass MBD- sequencing.

[0041] Any other method of DNA sequencing may also be used to sequence the cell-free DNA, including single-molecule real-time sequencing (e.g., Pacific Biosciences), ion semiconductor high-throughput sequence (e.g., Ion Torrent sequencing), pyrosequencing (e.g., 454 Life Sciences), sequencing by synthesis (Illummina), sequencing by ligation (SOLiD sequencing), nanopore sequencing, and chain termination sequencing (Sanger sequencing). Such methods are well-known in the art (e.g., see Heather et ak,“The sequence of sequencers: the history of sequencing DNA,” Genomics, 2016, 107(1): pp.1-8 and Kircher et al.,“High-throughput DNA sequencing-concepts and limitations,” Bioessays, 2010, 32(6): pp.524-536, each of which are incorporated herein by reference).

[0042] In various embodiments, the method involves analyzing a DNA sample to determine its methylation status, including the detection of a methylation marker which includes a DMR annoted to a gene, promoter, or transcription factor binding site. Any suitable method for analyzing DNA methylation status and the detection of DMRs is embraced by the instant methods, including, for example low-pass MBD-sequencing. Without being bound by theory, DNA methylation in vertebrates is characterized by the addition of a methyl or hydroxymethyl group to the C5 position of cytosine, which occurs mainly in the context of CpG dinucleotides. Non-CpG methylation in a CHH and CHG context (where H = A, C or T) also exists, typically seen in embryonic stem cells and in plants. The skilled person will appreciate that various methods of studying DNA methylation and detecting DNA methylated regions (e.g., DMRs) can be used, including (a) methyl- sensitive cut counting (MSCC), (b) luminometric methylation assay (LUMA), (c) long interspersed nuclear elements, (d) enzyme-linked immunosorbent assay (ELISA), (e) amplified fragment length polymorphism (AFLP), (f) restriction fragment length polymorphism (RFLP), (g) high resolution melting (HRM), and cold-PCR. Each of these methods are described in Kurdyukov et al.,“DNA methylation analysis: choosing the right method,” Biology (Basel), 2016, 5(1): p. 3, which is incorporated herein by reference. In addition, commerical kits for DNA methylation detection and analysis are well-known and can be used in conjunction with the herein methods.

[0043] Examples of such commercial kits are made by CELL BIOLABS (Global DNA

Methylation ELISA), SIGMA- ALDRICH (Imprint Methylated DNA Quantification Kit),

ABC AM (Epi Seeker Methylated DNA Quantification Kit), ACTIVE MOTIF (Global DNA Methylation Assay - Line 1), ZYMO RESEARCH (5-mC DNA ELISA Kit), and EPIGENTEK (MethylFlash Methylated DNA 5-mC Quantification Kit (Colorimetric) and MethylFlash Methylated DNA 5-mC Quantification Kit (Flourometric)), and INVITROGEN (MethylMiner™ Methylated DNA Enrichment Kit).

[0044] In certain embodiments, bisulfite sequencing is used to determine and/or detect DNA methylation in a DNA sample (e.g., to identify a DMR). Without being bound theory, the technique of bisulfite sequencing is considered to be the“gold standard” method in DNA methylation studies. Current DNA sequencing technologies do not possess the ability to distinguish methylcytosine from cytosine. The bisulfite treatment of DNA mediates the deamination of cytosine into uracil, and these converted residues will be read as thymine, as determined by PCR-amplification and subsequent Sanger sequencing analysis. However, 5 mC residues are resistant to this conversion and, so, will remain read as cytosine. Thus, comparing the Sanger sequencing read from an untreated DNA sample to the same sample following bisulfite treatment enables the detection of the methylated cytosines. With the advent of next- generation sequencing (NGS) technology, this approach can be extended to DNA methylation analysis across an entire genome. See Li P. et al,“An integrated workflow for DNA

methylation analysis,” J. Genet. Genomics ., 2013;40:249-260, the contents of which are incorporated herein by reference. In various other embodiments, the step of isolating cell-free DNA comprises methyl-CpG-binding domain-based (MBD) capture. Any suitable method of DNA isolation, and in particular, any suitable method of methylated DNA isolation, may be employed. Such methods will be well-known in the art (e.g., Soriano-Tarraga et al.,“DNA Isolation Method Is a Source of Global DNA Methylation Variability Measured with LUMA. Experimental Analysis and a Systematic Review,” PLoS ONE, 2016, 8(4): e60750.

[0045] Where the detection methods predict a risk for developing a pre-eclampsia associated condition (e.g., cardiovascular condition), the invention further contemplates the step of making a lifestyle modification. In various embodiments, the lifestyle modification may include one or more of the following recommendations from the American Heart Association’s Life’s Simple 7® checklist: (a) control cholesterol; (b) reduce blood sugar; (c) increase level/frequency of physical activity; (d) eat a healthier diet; (e) lose weight; and (f) cease smoking. In other embodiments, the lifestyle modification can comprise one or more of the following

recommendations from the Center for Disease Control: (a) limit tobacco use; (b) limit high blood pressure; (c) limit high blood cholesterol; (d) regulate type 2 diabetes; (e) eat a healthier diet; (f) avoid being overweight; and (g) increase level/frequency of physical activity.

[0046] These and other aspects of the technology are illustrated by the following non-limiting drawings, and described in more detail in the detailed description and examples.

BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure, which can be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein. [0048] FIGs. 1A-1H show concentration of circulating cfDNA in normal pregnancies and sPE. FIG. 1A shows that a methylated cfDNA library concentration (ng/pL) was quantified using a fluorometry-based method. (* p<0.05, Mann Whitney-U test). FIG. IB shows

electropherograms of high-sensitivity chips on an Agilent Bioanalyzer 2100. Representative samples from control and sPE methylated fractions are shown. The library fragment size is the sum of the plasma DNA molecules and the sequencing adaptor (121-122 bp). FIG. 1C depicts box and whiskers showing cell-free fetal DNA estimation. Mean coverage of Y chromosome calculated in pregnancies carrying male fetuses in controls (n=5) vs sPE (n=5). FIG. ID shows the mean coverage of RASSF1A regions and FIG. IE shows the mean coverage of SERPINB5 regions calculated in each sample (Controls, n=12; sPE, n=12). (p value calculated with Mann Whitney-U test). FIG. IF is a table showing differences in tissue regions coverage between sPE and controls in the significantly different cases. (SEM=standard error of the difference between means, SMC=vascular smooth muscle cells; p value was calculated with Wilcoxon test). FIG. 1G shows significantly different tissues. Y-axis represents the difference of the mean coverage of regions overlapping tissue references. The mean coverage of the control group was subtracted from the mean coverage of sPE. FIG. 1H depicts box and whiskers showing placental weight (g). Y-axis represents the weight in grams (p value was calculated with the Mann Whitney-U test).

[0049] FIGs. 2A-2E show a characterization of the differentially methylated regions (DMRs) in sPE vs controls. FIG. 2A shows hypermethylated regions annotated according to their genomic context. Percentage of regions was calculated using the total number of DMRs in each group. FIG. 2B shows that CpG context annotation of hypermethylated regions was calculated using the total number of DMRs that annotated to CpG context in each group. FIG. 2C shows enrichment analysis of KEGG pathways for the promoters hypermethylated in sPE are presented. The enrichment index was calculated via -log (p-value) and represented on the graphics. The number of genes that are implicated in each pathway are included in parenthesis. FIGs. 2D-2E show top 10 gene pathway sets (“GO (gene ontology) pathways”) in gene ontology analysis of the DMRs that annotated to transcription factor binding sites (TFBS) in sPE. There are 23 TFBS considered for each“up-represented” GO pathway (FIG. 2D) and 25 TFBS considered for each“down-represented” GO pathway (FIG. 2E). Blue squares indicate that a TFBS is related to the corresponding GO; white squares indicate that there is no relationship between TFBS and the corresponding GO term. [0050] FIGs. 3A-3B show a representation of DMRs that annotated to tissue-specific methylated regions. FIG. 3A shows in the vertical axis the number of DMRs that annotated to various genes from each tissue shown in the longitidinal axis (kidney, SMC, leuko, liver, pancreas, placenta, and skeletal muscle). The graph also shows frequency of DMR annotation to various components of the genes, including the 3’ end of the genes, as well as exons, intergenic regions, introns, and in the promoters of the genes. FIG. 3B shows the number of DMRs that annotated to promoters of genes involved in skeletal muscle, placenta, pancreas, liver, leukocyte, SMC, kidney, and aorta (see pie chart). In addition, the figure identifies various functional groups involved in the placenta / maternal-fetal interaction (e.g., decidua function, immune regulation, cell communication, vascular homeostatis, regulation of cell differentiation, and cell projection) having sets of genes in which DMRs were annotated to the promoters.

[0051] FIGs. 4A-4C show DMRs in genes and pathways involved in blood pressure control.

[0052] FIG. 4A shows renin-angiotensin-aldosterone system genes. Yellow circles indicates hypermethylated in sPE, while green circle indicates hypermethylation in the control group.

FIG. 4B shows NOS3 gene representation; numbers 1 to 7 represent each DMR. Box-plots represent methylation levels at each DMR. CpG track shows CpG islands present in that region. FIG. 4C is a scatter plot showing DMRl and DMR7 methylation level (x-axis) and systolic or diastolic blood pressure (y-axis).

[0053] FIG. 5 is a schematic of the study design. Maternal blood from controls (n=12) and severe pre-eclampsia (sPE; n=12) subjects were used for the plasma cell-free DNA (cfDNA) extraction (Table 8). Non-methylated and methylated fractions were obtained after the MBD enrichment and underwent deep sequencing and analysis.

[0054] FIGs. 6A-6B show transcription factor binding site (TFBS) regions that are over represented in control group DMRs (FIG. 6A) and over-represented in sPE DMRs (FIG. 6B). Y-axis shows number of DMRs, x-axis shows transcription factor names.

[0055] FIG. 7A is a schematic representation of RRBS ENCODE data processing. FIG. 7B shows the identification of specific-tissue regions. In the example, tissue 1 is compared with the rest of the tissues, and regions that are only present in tissue 1 are retained.

DETAILED DESCRIPTION OF THE INVENTION

[0056] The present invention is related, at least in part, on the discovery by the inventors that the presence, in maternal blood of a patient having pre-eclampsia, of methylated DNA (e.g., methylated cell-free DNA) derived from a tissue or organ (as opposed to from fetal tissue) can be indicative of long term damage to that tissue or organ. Thus, the specification describes and enables methods for evaluating, diagnosing and/or predicting the occurrence (or the downstream risk) of one or more systemic conditions associated with pre-eclampsia based upon the detection of one or more methylation biomarkers which correlate with said conditions. Accordingly the present invention provides systems, materials, and methylation markers for performing said detection methods. In certain embodiments, the methylation biomarkers are differentially methylated regions (DMRs) in a gene, a promoter of a gene, or a transcription factor binding site of a gene that is associated with a condition relating to pre-eclampsia. The present invention further relates to methods of treating a systemic condition associated with pre-eclampsia, or managing a heathy lifestyle that mitigates against a detected risk of developing such pre eclampsia associated conditions later in life. Further provided are methods for detecting a risk of developing a systemic condition associated with pre-eclampsia, comprising: (i) isolating cell- free DNA from a pre-eclampsia patient sample; (ii) analyzing the cell-free DNA for the presence of one or more methylation markers appearing on the cell-free DNA, wherein the one or more methylation markers are predictive of a condition associated with pre-eclampsia. In various embodiments, the one or more methylation markers annotates to a gene from Table 1. In various other embodiments, the one or more methylation markers is selected from the group of DMRs in Tables 2-7.

Definitions

[0057] Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this invention belongs. The following references provide one of skill in the art to which this invention pertains with a general definition of many of the terms used in this invention: Singleton et al, Dictionary of Microbiology and Molecular Biology (2d ed. 1994); The Cambridge Dictionary of Science and Technology (Walker ed., 1988); Hale & Marham, The Harper Collins Dictionary of Biology (1991); and Lackie et al., The Dictionary of Cell & Molecular Biology (3d ed. 1999); and Cellular and Molecular Immunology, Eds. Abbas, Lichtman and Pober, 2nd Edition, W.B. Saunders Company. For the purposes of the present invention, the following terms are further defined. [0058] As used herein and in the claims, the singular forms“a,”“an,” and“the” include the singular and the plural reference unless the context clearly indicates otherwise. Thus, for example, a reference to“an agent” includes a single agent and a plurality of such agents.

[0059] As used herein, the term“biomarker” or“biological marker” refers to a broad

subcategory of medical signs - that is, objective indications of medical state observed from outside the patient - which can be measured accurately and reproducibly. The term“biomarker” may also take on the definition provided by the National Institutes of Health Biomarkers Definitions Working Group defined a biomarker as“a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” Biomarkers are further defined and explained in Strimbu et ah,“What are Biomarkers?,” Curr Opin HIV AIDS, 2010, 5(6): 463- 466, the contents of which are incorporated herein by reference. Biological markers embraces, contemplates, or otherwise includes“methylation markers” or“methylation biomarkers.”

[0060] As used herein,“methylation marker” or“methylation biomarker” refers to any suitable biomarker based on one or more methylated bases in a nucleotide sequence which functions as an objective indication of a medical state, e.g., a condition associated with pre-eclampsia, such as, a cardiovascular condition (e.g., hypertension, stroke, arrhythmia, heart failure, or blood vessel disease). In some embodiments, one or more methylation markers may comprise differentially methylated regions (DMRs).

[0061] As used herein, the expression“differentially methylated region” or“DMR” refers to a category of methylation marker characterized as genomic regions with different DNA

methylation status across different biological samples (e.g., DNA from healthy versus diseased cell of tissue). DMRs may annote to a coding region of a gene. In other embodiments, DMRs may annote to a promoter of a gene. In still other embodiments, DMRs may annote to a transcription factor binding site of a gene. DMRs may be any suitable length (e.g., spanning between 2-500 nucleotides, or spanning between 10-400 nucleotides, or spanning between 50- 300 nucleotides, or spanning between 100-200 nucleotides). The DMRs may comprise contigous methylated nucleobases, or some pattern of interspersed methylated nucleobases.

[0062] As used herein, the term“cfDNA methylome” refers to the pattern of methylation occuring in the genome of a cell, and includes any DMR, if present.

[0063] As used herein, the term“cell-free DNA” or“cfDNA” refers to non-encapsulated DNA in the blood and/or other bodily fluids. cfDNA generally are thought to enter the blood during apoptosis or necrosis. The cfDNA isolated from blood usually contains fragments of about -170-500 bp thought to arise mostly from apoptotic cells. In addition, larger fragments (>1,000 bp) can also be present, which are thought to arise mostly from damaged cells, e.g., necrotic or apoptotic cells. The levels of cfDNA in plasma/serum are generally low in healthy individuals, however during pregnancy, illness, and periods of tissue damage or injury, the levels of cfDNA generally increase.

[0064] The term“subject” or“patient” refers to a human subject (e.g., a pregnant female).

[0065] As used herein, the term“sample” or“biological sample” or“test sample” refers to a sample, typically derived from a biological fluid, cell, tissue, organ, or organism, comprising a nucleic acid or a mixture of nucleic acids comprising at least one nucleic acid sequence. Such samples include, but are not limited to sputum/oral fluid, amniotic fluid, blood, a blood fraction, or fine needle biopsy samples (e.g., surgical biopsy, fine needle biopsy, etc.) urine, peritoneal fluid, pleural fluid, and the like.

Subjects

[0066] In some embodiments, a subject is a human subject (e.g., a pregnant female). In some embodiments, the pregnancy is a first pregnancy for the subject. In some embodiments, the pregnancy is a second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, or subsequent pregnancy for a subject. In some embodiments, the pregnancy results from in vitro fertilization.

[0067] In some embodiments, the subject develops signs or symptoms of pre-eclampsia during pregnancy. In some embodiments, the subject developed pre-eclampsia during one or more prior pregnancies. In some embodiments, the subject develops pre-eclampsia during a first pregnancy. In some embodiments, the subject develops pre-eclampsia for the first time after one or more prior pregnancies without pre-eclampsia.

[0068] In some embodiments, the subject of the present disclosure is a woman currently having pre-eclampsia during pregnancy, or a woman having had pre-eclampsia during a past pregnancy.

[0069] In some embodiments, a prior biological sample is obtained from a subject (e.g., from a subject who is not pregnant, for example before pregnancy or after a prior pregnancy, or from a pregnant subject) who has no signs or symptoms of pre-eclampsia. Such a sample can be used as a reference sample for that subject in the event the subject later develops pre-eclampsia (e.g., during a later pregnancy or during the same pregnancy). Alternatively, such a sample can be used as a reference sample for a different subject who has pre-eclampsia. In some embodiments, a biological sample can be obtained from a subject when one or more initial or early signs or symptoms of pre-eclampsia are detected. Such a sample also could be used as a reference sample to compare to a sample obtained from a later stage in pregnancy after a subject has developed later stage signs or symptoms of pre-eclampsia.

Biological Samples

[0070] Any suitable biological sample may be used in the present methods to evaluate and detect a current pre-eclampsia-associated condition, or the risk of developing a pre-eclampsia- associated condition later in life (e.g., a cardiovascular condition, such as hypertension, stroke, arrhythmia, heart failure, or blood vessel disease).

[0071] In an embodiment, the biological sample is blood.

[0072] In another embodiment, the biological sample is plasma.

[0073] In still another embodiment, the biological sample is from a bodily tissue or organ. The bodily tissue or organ can include brain, connective, bone, muscle, nervous system, lymph system, lungs, heart, blood vessels, stomach, colon, small intestine, pancreas, or gall bladder. Preferably, the sample is from a subject having or having had pre-eclampsia.

[0074] In some embodiments, a biological sample is obtained when a subject develops one or more signs or symptoms that are characteristic of pre-eclampsia. Such signs or symptoms can include high blood pressure (hypertension), proteinuria (protein in urine), edema (accumulation of excess fluid in hands, face, and elsewhere), headaches, nausea, vomiting, abdominal pain, shoulder pain, lower back pain, sudden weight gain, changes in vision, hyperreflexia, anxiety, and shortness of breath.

[0075] In some embodiments, a biological sample is obtained after subject has had one or more signs or symptoms of pre-eclampsia for at least several days (for example 2-5 days, 5-10 days, 1-2 weeks, 2-4 weeks, or longer).

[0076] In some embodiments, a sample is obtained from a subject at one or more of the following times during pregnancy: first trimester, second trimester, or third trimester, or at 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, or within the 9^th month of pregnancy. In some embodiments, a sample is obtained from a subject just before or after delivery.

[0077] As described herein, when a reference is made to obtaining or evaluating a biological sample it should be understood that one or more biological samples (e.g., two, three, four, five, six, seven, eight, nine, ten, or more biological samples) may be obtained or evaluated (e.g., for each subject). [0078] In some embodiments, a biological sample may be a blood sample. In some embodiments, a biological sample may be a non-blood sample.

[0079] In some embodiments, a sample may processed to remove cells in order to produce a cell-free sample (e.g., cell-free plasma or serum). In some embodiments, cells may be removed from a sample via centrifugation, chromatography, electrophoresis, or any other suitable method.

[0080] The biological samples may be used directly as obtained from the biological source or following a pretreatment to modify the character of the sample. For example, such pretreatment may include preparing plasma from blood, diluting viscous fluids and so forth. Methods of pretreatment may also involve, but are not limited to, filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, lyophilization, concentration, amplification, nucleic acid fragmentation, inactivation of interfering components, the addition of reagents, lysing, etc. If such methods of pretreatment are employed with respect to the sample, such pretreatment methods are typically such that the nucleic acid(s) of interest remain in the test sample, preferably at a concentration proportional to that in an untreated test sample (e.g., namely, a sample that is not subjected to any such pretreatment method(s)). Such“treated” or“processed” samples are still considered to be biological“test” samples with respect to the methods described herein.

[0081] In some embodiments, the sample is a mixture of two or more biological samples e.g. a biological sample can comprise two or more of a biological fluid sample, a tissue sample, and a cell culture sample. As used herein, the terms“blood,”“plasma” and“serum” expressly encompass fractions or processed portions thereof. Similarly, where a sample is taken from a biopsy, swab, smear, etc., the“sample” expressly encompasses a processed fraction or portion derived from the biopsy, swab, smear, etc.

[0082] In various embodiments, the biological sample (e.g., blood or plasma) is treated or processed by known methods to obtain the cell-free DNA present therein.

Cell-free DNA

[0083] In various aspects, the present invention involves the isolation, use, analysis, and epigenetic profiling of cell-free DNA (cfDNA).

[0084] Cell-free DNA can be isolated from any suitable biological sample, such as blood or plasma. In certain embodiments, the biological sample can be a cell-free sample. [0085] The isolation of cell-free DNA may be carried out by any suitable method, including centrifugation, chromatography, affinity binding, or any combination thereof. For example, a commercial kit may be used to isolate the cell-free DNA from a sample, e.g., Invitrogen

MadMAX magnetic bead technology. Further information on various kits that are suitable for isolating cell-free DNA are described in the literature, including Sorber et al, A Comparison of Cell-Free DNA Isolation Kits: Isolation and quantification of Cell-Free DNA in Plasma, J. Mol. Diagn., 2017, pp 162-168.

[0086] Cell-free nucleic acids, including cell-free DNA, can be obtained by various methods known in the art from biological samples including but not limited to plasma, serum, and urine (see, e.g., Fan et al., Proc Natl Acad Sci 105: 16266-16271 [2008]; Koide et al., Prenatal Diagnosis 25:604-607 [2005]; Chen et al., Nature Med. 2: 1033-1035 [1996]; Lo et al., Lancet 350: 485-487 [1997]; Botezatu et al., Clin Chem. 46: 1078-1084, 2000; and Su et al., J. Mol. Diagn. 6: 101-107 [2004]). To separate cell-free DNA from cells in a sample, various methods including, but not limited to fractionation, centrifugation (e.g., density gradient centrifugation), DNA-specific precipitation, or high-throughput cell sorting and/or other separation methods can be used. Commercially available kits for manual and automated separation of cfDNA are available (Roche Diagnostics, Indianapolis, Ind., Qiagen, Valencia, Calif., Macherey-Nagel, Duren, Del.). Biological samples comprising cfDNA have been used in assays to determine the presence or absence of chromosomal abnormalities by sequencing assays that can detect chromosomal aneuploidies and/or various polymorphisms.

[0087] In other embodiments, cell-free DNA is obtained from suitable biological liquid, including but not limited to blood, serum, plasma, urine, salvia, ascites, and pleural effusia. In still other embodiments, cfDNA represents an accessible sample of DNA circulating

systematically in the patient or subject, and can thus be used to access DNA originating in a range of different tissues across the body without the need to sample cellular DNA from those tissues and locations.

[0088] In still other embodiments, 1 to 20 mL blood draws can provide from 1 to 100 ng of cfDNA.

[0089] In some embodiments, the pre-processing of samples to enrich for nucleic acids using bead or polymers based methods can also be implemented to provide a higher yield to facilitate cfDNA analyses.

[0090] It will be appreciated that since cfDNA is known to be prone to degradation in biological samples due to the unsuitable environment of these samples (e.g., unsuitable pH), care should be taken so as to avoid unnecessary degradation. In some embodiments, the cfDNA of the disclosure comprises DNA fragments between 25 base pairs (bp) and 350 bp. In some embodiments, the cfDNA comprises DNA fragments between 25-100 bp in length. In some embodiments, the cfDNA comprises fragments of about 25bp, 30bp, 40bp, 50bp, 75bp, lOObp, 150bp, 200bp, 250bp, 300bp or 350bp in length.

[0091] In some embodiments, cfDNA originating in tissues experiencing apoptotic or necrotic cell damage will be enriched in the overall pool of cfDNA. In some embodiments, specialized collections procedures are used isolate cfDNA, including but not limited to efforts to isolate clean plasma samples without leucocytes or other whole cells which might otherwise lyse during sample process, and dilute the systematic cfDNA sample with genomic cellular DNA from cells (a,c) contaminating the samples. In some embodiments, high molecular weight DNA can be eliminated from the sample to assure cfDNA purity or alternately a threshold level of high molecular weight DNA can be used as a quality control marker to reject cfDNA samples contaminated with cellular DNA. In some embodiments, cfDNA samples can be addressed by direct amplification and sequencing by PCR based methods where the gene or genes of interest are known. In some embodiments, hybridization arrays can be used to capture multiple targets of interest from cfDNA preparations which can then be addressed further with next generation sequencing methods or conventional PCR-type methods.

[0092] In some embodiments, specialized sample collection methods including but not limited to EDTA stabilized plasma samples or samples fixed with diazolidinyl urea, imidazolidinyl urea or other formalin-releasing agents (e.g., paraformaldehyde) are used to minimize cell lysis and enhance the stability and thus improve the logistics of sample collection and handing.

[0093] In some embodiments, the very short half-life of cfDNA (less than one hour) can be used to measure rapidly changing processes or the immediate impact of interventions with great temporal precision.

[0094] Generally, samples of isolated DNA may be fragments and separated into methylated and unmethylated DNA portions. A number of methods can be used to separate DNA into methylated or unmethylated DNA portions. In some embodiments, this can be achieved, for example, by cleaving the fragmented genomic DNA of a uniform length with a methyl-sensitive (or alternatively a methyl-dependent) restriction endonuclease to separate one or two sub portions: a sub-portion of uncleaved DNA molecules and a sub-portion of cleaved DNA molecules. When methyl-dependent restriction enzymes are used (cleaving methylated sequences but not unmethylated sequences), the sub-portion of uncleaved DNA fragments will represent unmethylated restriction sequences and the sub-portion of cleaved DNA fragments will represent methylated restriction sequences. Conversely, when a methyl- sensitive restriction enzyme is used (cleaving unmethylated sequences but not methylated sequences), the sub portion of uncleaved DNA fragments will represent methylated restriction sequences and the sub-portion of cleaved DNA fragments will represent unmethylated restriction sequences.

[0095] A number of methyl-dependent and methyl-sensitive restriction enzymes are known to those of skill in the art. Restriction enzymes can generally be obtained from, e.g., New England Biolabs (Beverly, Mass.) or Roche Applied Sciences (Indianapolis, Ind.). Exemplary methyl- dependent restriction enzymes include, e.g., McrBC, McrA, MrrA, and Dpnl. Exemplary methyl- sensitive restriction enzymes include, e.g., Pstl, BstNI, Fsel, Mspl, Cfol, and Hpall. See e.g., McClelland, M. et al, Nucleic Acids Res. 1994 Sep;22(17):3640-59 and

http://rebase.neb.com.

[0096] The two cleaved and uncleaved populations of DNA can be separated by molecular weight using a number of methods known to those of skill in the art. For example, gel electrophoresis, size exclusion chromatography, size differential centrifugation (e.g., in a sucrose gradient) can be used to separate cleaved fragments from heavier uncleaved fragments.

[0097] Those of skill in the art will recognize that other methods of separating methylated and unmethylated populations, thereby depleting the sample of methylated or unmethylated DNA, can also be used. For example, antibodies or other agents (e.g., MeCP2) specific for methylated nucleic acids or proteins associated with methylated nucleic acids can be used to affinity purify the methylated nucleic acids, thereby separating the methylated DNA from unmethylated DNA. See, e.g., Meehan, et al., Nucleic Acids Res. 20(19):5085-92 (1992). In this case, the DNA can, but need not, be cleaved with a restriction endonuclease that senses methylation. In some embodiments, for example, an affinity column comprising a protein specific for methylated DNA is used to separate methylated and unmethylated fractions. Once separated into fractions, either fraction or both fractions can be labeled for hybridization or sequencing.

[0098] In other embodiments, chemical agents, alone or in concert with enzymes, capable of specifically cleaving methylated nucleic acids are used to generate methylated and unmethylated populations. The populations can then be separated as described above.

[0099] DNA methylation is a ubiquitous biological process that occurs in diverse organisms ranging from bacteria to humans. During this process, DNA methyltransferases catalyze the post-replicative addition of a methyl group to the N⁶ position of adenine or the C⁵ or N⁴ position of cytosine, for which S-adenosylmethionine is the universal donor of the methyl group. In higher eukaryotes, DNA methylation plays a role in genomic imprinting and embryonic development, as well as regulation of gene expression. In addition, aberrations in DNA methylation have been implicated in aging and various diseases including cancer.

[0100] In some embodiments, the cfDNA may be separated into methylated nucleic acid (e.g., methylated DNA) may be isolated from a sample (e.g., from a cell free nucleic acid sample or directly from a biological sample obtained from a subject). In some embodiments, a cell free nucleic acid sample may be enriched for methylated nucleic acid (e.g., methylated DNA) using a technique that preferentially isolates methylated nucleic acid. For example, an affinity technique (e.g., affinity chromatography) using an agent (e.g., an antibody) that preferentially binds methylated nucleic acid may be used. Other methods described above may be used to enrich for methylated DNA.

Methylation Detection

[0101] In various aspects, the cell-free DNA may be analyzed to identify the methylation patterns or profile, e.g., to identify one or more DMRs.

[0102] In some embodiments, a cell-free nucleic acid sample (e.g., a cell-free nucleic acid sample that has not been enriched for methylated nucleic acid, a cell-free methylated nucleic acid sample, or a cell-free nucleic acid sample enriched for methylated nucleic acid) may be evaluated to determine the pattern of methylation that is present. In some embodiments, any suitable technique for determining the presence or one or more methylation patterns may be used.

[0103] Without being bound by theory, DNA methylation in vertebrates is characterized by the addition of a methyl or hydroxymethyl group to the C5 position of cytosine, which occurs mainly in the context of CpG dinucleotides. Non-CpG methylation in a CHH and CHG context (where H = A, C or T) also exists, typically seen in embryonic stem cells and in plants. The skilled person will appreciate that various methods of studying DNA methylation and detecting DNA methylated regions (e.g., DMRs) can be used, including (a) methyl-sensitive cut counting (MSCC), (b) luminometric methylation assay (LUMA), (c) long interspersed nuclear elements, (d) enzyme-linked immunosorbent assay (ELISA), (e) amplified fragment length polymorphism (AFLP), (f) restriction fragment length polymorphism (RFLP), (g) high resolution melting (HRM), and cold-PCR. Each of these methods are described in Kurdyukov et ah,“DNA methylation analysis: choosing the right method,” Biology (Basel), 2016, 5(1): p. 3, which is incorporated herein by reference. In addition, commerical kits for DNA methylation detection and analysis are well-known and can be used in conjunction with the herein methods.

[0104] Examples of such commercial kits are made by CELL BIOLABS (Global DNA

Methylation ELISA), SIGMA- ALDRICH (Imprint Methylated DNA Quantification Kit), ABCAM (EpiSeeker Methylated DNA Quantification Kit), ACTIVE MOTIF (Global DNA Methylation Assay - Line 1), ZYMO RESEARCH (5-mC DNA ELISA Kit), and EPIGENTEK (MethylFlash Methylated DNA 5-mC Quantification Kit (Colorimetric) and MethylFlash Methylated DNA 5-mC Quantification Kit (Flourometric)), and INVITROGEN (MethylMiner™ Methylated DNA Enrichment Kit).

[0105] In certain other embodiments, bisulfite sequencing is used to determine and/or detect DNA methylation in a DNA sample (e.g., to identify a DMR). Without being bound theory, the technique of bisulfite sequencing is considered to be the“gold standard” method in DNA methylation studies. Current DNA sequencing technologies do not possess the ability to distinguish methylcytosine from cytosine. The bisulfite treatment of DNA mediates the deamination of cytosine into uracil, and these converted residues will be read as thymine, as determined by PCR-amplification and subsequent Sanger sequencing analysis. However, 5 mC residues are resistant to this conversion and, so, will remain read as cytosine. Thus, comparing the Sanger sequencing read from an untreated DNA sample to the same sample following bisulfite treatment enables the detection of the methylated cytosines. With the advent of next- generation sequencing (NGS) technology, this approach can be extended to DNA methylation analysis across an entire genome. See Li P. et al,“An integrated workflow for DNA

[0106] In other embodiments, the analysis of methylation provides a means to examine the epigenetic regulation of gene expression which is related to the methylation of cytosines in DNA sequences. In some embodiments, the pattern of gene regulation detected by the pattern of cytosine methylation can be augmented by the analysis of other epigenetic modifications detectable in cfDNA, including but not limited to the modification of the DNA base thymine to 5-hydroxymethyluracil.

[0107] In still other embodiments, disease-specific DNA methylation indicating a pathologic process (or risk thereof) can be used as a biomarker of disease or pathology (or risk thereof) of interest, particularly where bioinformatic or other methods are used to sort the systematic cfDNA signal from the high background signal specific to the sample type. For example, where the sample type is plasma or serum, most cfDNA originates from white blood cells, and unless the methylation patterns of leukocytes is the methylome of interest, the white blood cell signal needs to be removed from the data set.

[0108] In various embodiments, once the nucleic acids have been extracted, methylation analysis is carried out by any means known in the art. A variety of methylation analysis procedures are known in the art and may be used to practice the methods disclosed herein. These assays allow for determination of the methylation state of one or a plurality of CpG sites within a tissue sample. In addition, these methods may be used for absolute or relative quantification of methylated nucleic acids.

[0109] Some methylation assays involve, among other techniques, two major steps. The first step is a methylation specific reaction or separation, such as (i) bisulfite treatment, (ii) methylation specific binding, or (iii) methylation specific restriction enzymes. The second major step involves (i) amplification and detection, or (ii) direct detection, by a variety of methods such as (a) PCR (sequence-specific amplification) such as Taqman®, (b) DNA sequencing of untreated and bi sulfite-treated DNA, (c) sequencing by ligation of dye-modified probes

(including cyclic ligation and cleavage), (d) pyrosequencing, (e) single-molecule sequencing, (f) mass spectroscopy, or (g) Southern blot analysis.

[0110] Other methods for DNA methylation analysis include restriction landmark genomic scanning (RLGS, Costello et al, 2002, Meth. Mol Biol, 200, 53-70), methylation-sensitive- representational difference analysis (MS-RDA, Ushijima and Yamashita, 2009, Methods Mol Biol 507, 1 17-130). Comprehensive high-throughput arrays for relative methylation (CHARM) techniques are described in WO 2009/021141 (Feinberg and Irizarry). The Roche®

NimbleGen® microarrays including the Chromatin Immunoprecipitation-on-chip (ChIP-chip) or methylated DNA immunoprecipitation-on-chip (MeDIP-chip). These tools have been used for a variety of cancer applications including melanoma, liver cancer and lung cancer (Koga et al, 2009, Genome Res., 19, 1462-1470; Acevedo et al, 2008, Cancer Res., 68, 2641-2651; Rauch et al, 2008, Proc. Nat. Acad. Sci. USA, 105, 252-257). Others have reported bisulfate conversion, padlock probe hybridization, circularization, amplification and next generation or multiplexed sequencing for high throughput detection of methylation (Deng et al, 2009, Nat. Biotechnol 27, 353-360; Ball et al, 2009, Nat. Biotechnol 27, 361-368; U.S. Pat. No. 7,611,869 (Fan)). As an alternative to bisulfate oxidation, Bayeyt et al. have reported selective oxidants that oxidize 5- methylcytosine, without reacting with thymidine, which are followed by PCR or pyro sequencing (WO 2009/049916 (Bayeyt et al).

[0111] Other aspects of the invention involve determining the nucleotide sequence of cell-free DNA. In one embodiment, the methods described herein can utilize next generation sequencing technologies that allow multiple samples to be sequenced individually as genomic molecules (e.g., singleplex sequencing) or as pooled samples comprising indexed genomic molecules (e.g., multiplex sequencing) on a single sequencing run. These methods can generate up to several hundred million reads of DNA sequences. In various embodiments the sequences of genomic nucleic acids, and/or of indexed genomic nucleic acids can be determined using, for example, the Next Generation Sequencing Technologies (NGS) described herein. In various embodiments analysis of the massive amount of sequence data obtained using NGS can be performed using one or more processors as described herein.

[0112] 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.

[0113] 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.

[0114] In one illustrative approach, sequencing library preparation involves 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.

[0115] 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 pregnant subject.

[0116] 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. [0117] Methods and further information regarding purification, processing, sequence, and analyzing cfDNA can be found in the following references, each of which are incorporated herein by reference:

[0118] Yigit B, Boyle M, Ozler O, et al.“Plasma cell-free DNA methylation: a liquid biomarker of hepatic fibrosis,” Gut, Published Online 20 January 2018;

[0119] Lehmann- Werman R et al,“Identification of tissue-specific cell death using methylation patterns of circulating DNA,” Proc Natl Acad Sci USA 2016; 113 :E1826-34;

[0120] Hardy T et al,“Plasma DNA methylation: a potential biomarker for stratification of liver fibrosis in non-alcoholic fatty liver disease,” Gut ,2016, Epub ahead of print 21 Mar 2016;

[0121] Cui M et al,“Cell-Free circulating DNA: a new biomarker for the acute coronary syndrome,” Cardiology 2013; 124:76-84;

[0122] Zhong XY et al.,“Increased concentrations of antibody-bound circulatory cell-free DNA in rheumatoid arthritis,” Clin Chem 2007;53 : 1609-14;

[0123] Bartoloni E et al,“Increased levels of circulating DNA in patients with systemic autoimmune diseases: a possible marker of disease activity in Sjogren’s syndrome,” Lupus 2011;20:928-35;

[0124] Rainer TH, Wong LK, Lam W, Yuen E, Lam NY, Metreweli C, et al. Prognostic use of circulating plasma nucleic acid concentrations in patients with acute stroke. Clin Chem

2003;49:562-9. Google Scholar

[0125] Antonatos D, Patsilinakos S, Spanodimos S, Korkonikitas P, Tsigas D. Cell-free DNA levels as a prognostic marker in acute myocardial infarction. Ann N Y Acad Sci 2006; 1075:278- 81. Google Scholar

[0126] Jiang P, Lo YM. The long and short of circulating cell-free DNA and the Ins and outs of molecular diagnostics. Trends Genet 2016;32:360-71. Google Scholar

[0127] Wou K, Feinberg JL, Wapner RJ, Simpson JL. Cell-free DNA versus intact fetal cells for prenatal genetic diagnostics: what does the future hold? Expert Rev Mol Diagn 2015; 15:989-98. Google Scholar

[0128] Benn P. Non-invasive prenatal testing using cell free DNA in maternal plasma: recent developments and future prospects. J Clin Med 2014;3 :537-65. Google Scholar

[0129] Bianchi DW, Chudova D, Sehnert AJ, Bhatt S, Murray K, Prosen TL, et al. Noninvasive prenatal testing and incidental detection of occult maternal malignancies. J Am Med Assoc 2015;314: 162-9. Google Scholar [0130] Hahn S, Rusterholz C, Hosli I, Lapaire O. Cell-free nucleic acids as potential markers for preeclampsia. Placenta 2011;32(Suppl):S17-20. Google Scholar

[0131] Ryan WL. Method and device for collecting and preserving cells for analysis. US Patent Application 2010;0317107:A1. Google Scholar

[0132] Holmes EE, Jung M, Meller S, Leisse A, Sailer V, Zech J, et al. Performance evaluation of kits for bisulfite-conversion of DNA from tissues, cell lines, FFPE tissues, aspirates, lavages, effusions, plasma, serum, and urine. PLoS One 2014;9:e93933. Google Scholar

[0133] Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW, et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual

DNA strands. Proc Natl Acad Sci USA 1992;89: 1827-31. Google Scholar

[0134] Holmes EE, Jung M, Meller S, Leisse A, Sailer V, Zech J, et al. Performance evaluation of kits for bisulfite-conversion of DNA from tissues, cell lines, FFPE tissues, aspirates, lavages, effusions, plasma, serum, and urine. PLoS One 2014;9:e93933. Google Scholar

[0135] In other embodiments, the DNA methylation patterns can be used to detect or diagnose tissue specific disorders or pathologies because the cfDNA can be traced back to its tissue of origin. Because a pathologic process leading to tissue damage will release tissue-specific cfDNA, this can be detected by methylation analyses of the circulating or systematic fraction of cfDNA.

[0136] The effect of DNA methylation on gene expression is both regional and profound.

Alterations in genomic methylation give rise to the inappropriate expression of neighboring genes. Consequently, the ability to survey the methylation states of multiple regions of the genome (or determine a‘methylation profile’) allows for the association of specific methylation states with gene expression and or traits.

[0137] The present invention provides methods of determining methylation profiles of nucleic acids, including methylation profiles of entire genomes. In various aspects, methylation profiling can involve the step of generating a uniformly-sized population of fragmented (e.g., randomly cleaved or sheared) DNA and generating DNA samples consisting of methylated and/or unmethylated DNA. Methylation profiles of a nucleic acid can then be determined by quantifying the relative amounts of the nucleic acid between any two of the following: total DNA, methylated DNA or unmethylated DNA, i.e., samples depleted for unmethylated or methylated DNA, respectively.

[0138] Methylation profiles can be detected in a number of additional ways known to those of skill in the art. For example, simple hybridization analysis (e.g., Southern blotting) of nucleic acids cleaved with methyl- sensitive or methyl-dependent restriction endonucleases can be used to detect methylation patterns. Typically, these methods involve use of one or more targets that hybridize to at least one sequence that may be methylated. The presence or absence of methylation of a restriction sequence is determined by the length of the polynucleotide hybridizing to the probe. This and other methods for detecting DNA methylation, such as bisulfite sequencing, are described in, e.g., Thomassin et al., Methods 19(3):465-75 (1999). Methylation Markers

[0139] In various embodiments, the invention relates to one or more methylation markers (e.g., differentially methylated regions or DMRs) that are indicative of a pre-eclampsia-related condition, e.g., a cardiovascular or kidney disorder. In certain embodiments, the methylation markers can occur in the promotor of a gene, e.g., a promoter of a gene or in a transcription factor binding site of a gene associated with cardiovascular health or kidney health. In various embodiments, the differentially methylated region (DMR) is a hypermethylated region. In other embodiments, the differentially methylated region (DMR) is a hypomethylated region.

[0140] In some embodiments, a nucleic acid sample (e.g., a cell-free nucleic acid sample) may contain a mixture of patterns characteristic of different tissues or organs. For example, if one or more tissues or organs are damaged in a subject as a result of pre-eclampsia, then cell-free nucleic acid released from the one or more tissues or organs may be detected in a sample being analyzed. Accordingly, in some embodiments a nucleic acid methylation pattern detected in a biological sample may be a combination of methylation patterns from two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) different tissues or organs. In some embodiments, methods of the disclosure include methods of analyzing a nucleic acid methylation pattern detected in a biological sample to identify the one or more different tissue-specific methylation patterns that are present in the sample.

[0141] In other embodiments, the one or more methylation markers is a DMR in a gene (e.g., in the promoter thereof) involved in pre-eclampsia, type 2 diabetes, chronic renal failure, hypertension, liver metabolic dysfunction, HDL cholesterol levels, LDL cholesterol levels, metabolic syndrome, or chronic kidney disease. In various embodiments, the methylation markers are have a fold change of at least 2-fold greater than in a comparison control tissue that does not have the disease.

[0142] In various embodiments, the one or more methylation markers is annoted to a gene from the renin-angiotensin-aldosterone system (RAAS), such as“STK40” “STK39” ΈNRER” “CYP11B2”“SCNN1B”“NEDD4L”“SCNN1D”“NOS3” “PRCP” “SCNN1A” and

“ACE”,“ENPEP” “CYP11B2”“SCNN1B”“NEDD4L”“SCNN1D”“NOS3” “PRCP” “SCNN1A”“ACE”. In various other embodiments, the one or more methylation markers is annoted to a promoter of a gene from the renin-angiotensin-aldosterone system (RAAS), such as “STK40” “STK39” “ENPEP” “CYP11B2”“SCNN1B”“NEDD4L”“SCNN1D”“NOS3” “PRCP” “SCNN1A” and“ACE”,“ENPEP” “CYP11B2”“SCNN1B” “NEDD4L” “SCNN1D”“NOS3” “PRCP” “SCNN1A”“ACE”. In still other embodiments, the one or more methylation markers is annoted to a transcription factor binding site of a gene from the renin-angiotensin-aldosterone system (RAAS), such as“STK40” “STK39” “ENPEP” “CYP11B2”“SCNN1B”“NEDD4L”“SCNN1D”“NOS3” “PRCP” “SCNN1A” and “ACE”,“ENPEP” “CYP11B2”“SCNN1B”“NEDD4L”“SCNN1D”“NOS3” “PRCP” “SCNN1A”“ACE”.

[0143] In various other embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene implicated in immune regulation. Such genes can include, for example, IRF4, HLA-A, HLA-D, and TYK2. In one embodiment, the methylation marker occurs in the promoter region of the indicated genes.

[0144] In various other embodiments, the one or more methylation markers is implicated in blood circulation and annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene selected from the group consisting of: CD 28, NKX2.5, ASIC 2, CAMK2D, GAS6, GUCY1B3, and PDE4D. In one embodiment, the methylation marker occurs in the promoter region of the indicated genes.

[0145] In still other embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene selected from the group consisting of: NRXN1, CAMK2D, EPHA2, KIF5C, ASIC2, CRHBP, KCNA1, SPOCK1, ITGA8, SLC5A7, CYNC1H1, SHANK1, CD302, OLFM1, MAP IS, PDE1C, ALCAM, DZIP1, RGMA, and MLPH. In one embodiment, the methylation marker occurs in the promoter region of the indicated genes.

[0146] In still further embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene implicated in endometrial decidualization and meterno-fetal interactions, including ALCAM and GDF10.

[0147] In still further embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene implicated in trophoblast invasion and differentiation, including TLX1, HOXA9, CD38, LPL and NKX2.5. [0148] In still other embodiments, the one or more methylation markers annotates to a gene, a promoter of a gene, or a transcription factor binding site of a gene selected from the group consisting of: ENaC, CYP11B2, STK9, NEDD4L,NOS3, or SCNN1D, and predictive of high blood pressure.

[0149] In other embodiments, the one or more methylation markers is a DMR occurring in a transcription factor binding site (TFBS) of a gene. Exemplary DMRs annotate to various TFBS sequences including, but are not limited to, HDAC1, SIN3A, CTCFL, SUZ12, EXH2, BABPA, TAF7, TCF3, EGR1, NRF1, ZBTB7A, HMBN3, CHD1, SAP 30, CTBP2, CCN72, E2F6, SP4, HDAC8, RBBPS, UBTF, PHF8, and KDM5B.

[0150] In other embodiments, the one or more methylation markers is a DMR occurring in a transcription factor binding site (TFBS) of a promoter of a gene. Exemplary DMRs annotate to various TFBS sequences including, but are not limited to, GATZ3, MEF2C, JUN, SP11, STAT3, CEB28, IRF4, FOS, BATF, GATA2, MAFK, ATF1, ARID3A, BCL11A, P05F1, FOXA2, TALI, PROM1, IRF3, ZNF217, FOXA1, MAFF, NFE2, and FAM48A.

[0151] In some embodiments, the one or more methylation markers is a DMR associated with gene pathways involved in the maternal-fetal interaction. In one aspect, the DMRs are associated with the following genes involved in cell projection: NRXN1, CAMK2D, EPHA2, KIF5C,

ASIC2, CRHBP, KCNA1, SPOCK1, ITGA8, SLC5A7, DYNC1H1, SHANK1, CD302, OLFM1, MAP IS, PDE1C, ALCAM, DZIP1, RGMA, and MLPH. In one embodiment, the DMRs are associated with the following genes involved in regulation of cell differentiation: SFMA6D, PLXNA4, NKX2-5, OLFM1, OBSL1, SHANK1, NRG3, and GAS6. In another aspect, the DMRs are associated with the following genes involved in vascular homeostasis: CD38, NKX2.5, ASIC2, CAMK2D, CAS6, GUYCY1B3, and PDE4D. In other embodiments, the DMRs are associated with the following genes involved in cell communication: NKX2.5, VWC2, EPHA2, FOXP1, TBX18, and CELF4. In still other embodiments, the DMRs are associated with the following genes involved in immune regulation: IRF4, HLA-A, HLA-B, HLA-D, TYK2, CAM2D, TCF3, and PRDM16. In still other embodiments, the DMRs are associated with the following genes involved in decidua function: ALCAM, GDF10, TLX1, HOXA9, CD38, and NKX2-5.

[0152] In some embodiments, the one or more methylation markers is a DMR associated with a gene of Table 1 relating to hypertension, chronic kidney failure, glomerulosclerosis, acute kidney injury, or glomerulonephritis.

[0153] Table 1 - List of genes relating to hypertension, chronic kidney failure,

glomerulosclerosis, acute kidney injury, or glomerulonephritis.

[0154] In other embodiments, the methods disclose herein involve methylation markers of Table

2. Table 2 lists DMRs that annotate to genes of Table 1 (i.e., those genes which relate to hypertension, chronic kidney failure, glomerulosclerosis, acute kidney injury, or

glomerulonephritis) .

[0155] Table 2 - DMRs which annotate to the genes of Table 1. The annotation may be to the promoter, intron, 3' end, or exon of said genes. The DMR may be a hyperm ethylation in the sPE (bolded text) source or a hypermethylation in the control (non-bolded text).

[0156] In other embodiments, the methods involve methylation markers of Table 3. Table 3 lists DMRs that annotate to genes exclusive to the group. That is, the DMRs annotate either as hypermethylated regions in the Control group (non-bolded text) or as hypermethylated regions in sPE samples (bolded text). [0157] Table 3 - DMRs which annotate either as hypermethylated regions in the control group (non-bolded text) or as hypermethylated regions in sPE samples (bolded text).

[0158] In other embodiments, the methods involve methylation markers of Table 4. Table 4 lists DMRs that annotate to genes of the renin-angiotensin-aldosterone system (RAAS). That is, the DMRs annotate either as hypermethylated regions in the control group (non-bolded text) or as hypermethylated regions in sPE samples (bolded text).

[0159] Table 4 - DMRs that annotate to genes of the renin-angiotensin-aldosterone system (RAAS).

[0160] In other embodiments, the methods involve methylation markers of Table 5. Table 5 lists DMRs that annotate to transcription factor binding sites (“TF”) in the PRCP gene.

[0161] Table 5 - DMRs that annotate to transcription factor binding sites (“TF”) in the PRCP gene.

[0162] In other embodiments, the methods involve methylation markers of Table 6. Table 6 lists DMRs that annotate to transcription factor binding sites (“TF”) in the ACE gene.

[0163] Table 6 - DMRs that annotate to transcription factor binding sites (“TF”) in the ACE gene.

[0164] In still other embodiments, the methods involve methylation markers of Table 7. Table 7 lists DMRs that annotate to transcription factor binding sites (“TF”) in the NOS3 gene. [0165] Table 7 - DMRs that annotate to transcription factor binding sites (“TF”) in the NOS3 gene.

Conditions Associated With Pre-Eclampsia

[0166] Pre-eclampsia is a systemic condition that has significant downstream effects on a woman’s health throughout her life. For example, it is generally recognized the pre-eclampsia doubles a woman’s risk of heart disease and stroke, and quadruples a woman’s risk of high blood pressure (i.e., hypertension) later in life. Indeed, two out of three women who experience pre-eclampsia are predicted to die from cardiovascular disease.

[0167] More specifically, pre-eclampsia is a syndrome defined by pregnancy-induced hypertension and proteinuria (protein the urine), which can lead to eclampsia (convulsions), and other serious maternal and/or fetal complications. Preeclampsia is thought to be closely related to complications of pregnancy in early gestation, such as, implantation failure. Pre-eclampsia affects approximately 5-7% of pregnant women and is an important cause of maternal and perinatal mortality. Furthermore, not only do women with pre-eclampsia have a higher risk of cardiovascular death later in their life, their offspring bom from pregnancies affected by pre eclampsia have an increased risk of metabolic and cardiovascular disease and mortality later in life.

[0168] The methods disclosed herein may lead to the identification of an existing condition or the risk of developing such a condition which is associated with pre-eclampsia, such as a cardiovascular condition (e.g., hypertension, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia) or a chronic kidney condition (e.g., chronic kidney disease (CKD)). Once a subject having had pre-eclampsia is diagnosed with such a condition, or is diagnosed with a risk of developing such as condition, the subject can proceed to be treated in several ways, depending on whether the condition an existing one or there is a risk of developing the condition.

[0169] There is a significant need for the identification and/or development of biomarkers that would enable the early and molecular signature-based diagnosis and prediction of the complications of pregnancy, as well as the development of drugs with specific effects on the pathologic pathways would be of major societal and economic importance.

Treatment Methods

[0170] The methods disclosed herein may lead to the identification of an existing condition or the risk of developing such a condition which is associated with pre-eclampsia, such as a cardiovascular condition (e.g., hypertension, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia) or a chronic kidney condition (e.g., chronic kidney disease (CKD)). Once a subject having had pre-eclampsia is diagnosed with such a condition, or is diagnosed with a risk of developing such as condition, the subject can proceed to be treated in several ways, depending on whether the condition an existing one or there is a risk of developing the condition.

[0171] In some embodiments, the subject will be diagnosed with having one or more conditions associated with pre-eclampsia. Such a subject, once identified, may be treated by a standard-of- care therapy, depending upon the condition that is diagnosed.

[0172] In one embodiment, the subject may be diagnosed with an existing cardiovascular condition that is associated with pre-eclampsia. Such conditions may include, for example, hypertension, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia. In such embodiments, the subject may be administered a therapeutically effective amount of standard-of-care therapy. For example, hypertension may be treated by administering a therapeutically effective amount of an antihypertensive drug. Antihypertensives are a class of drugs that are used to treat hypertension (high blood pressure). There are many classes of antihypertensives, which lower blood pressure by different means. Antihypertensives include, but are not limited to, ACE inhibitors (angiotensin-converting enzyme inhibitors), ARBs (angiotensin II receptor blockers), beta blockers, calcium channel blockers, direct renin inhibitors, and diuretics.

[0173] In another embodiment, the subject may be diagnosed with risk of stroke, for which a standard-of-care treatment may be administered. Treatments for stroke vary depending on whether the stroke is caused by a blood clot (ischemic stroke) or a brain bleed (hemorrhagic stroke). Exemplary treatments may include a therapeutically effective amount of tissue plasminogen activator and aspirin for ischemic strokes. For hemorrhagic strokes, one or more surgical treatments could be administered, e.g., endovascular procedure (i.e., insertion of a long tube into a blood vessel in an arm or leg, and passed all the way up to the blood vessels in the brain, where a coil or clip is placed to prevent further bleeding), surgical clipping (placement of a tiny clamp at its base to stop an aneurysm), or sterotactic radiosurgery (a minimally invasive technique that uses highly focused radiation to repair vascular malformations).

[0174] In another embodiment, the subject may be diagnosed with an arrhythmia, for which a standard-of-care antiarrhythmic drug may be administered. Such drugs may include calcium channel blockers, beta-blockers, and anticoagulants.

[0175] In still another embodiment, the subject may be diagnosed with atherosclerosis, for which a standard-of-care drug may be administered. Such drugs may include, for example, statins (for lowering“bad” LDL cholesterol, e.g., Atorvastatin (Lipitor®), Fluvastatin

(Lescol®), Lovastatin (Altoprev®, Mevacor®), Pitavastatin (Livalo®), Pravastatin (Pravachol®), Rosuvastatin calcium (Crestor®), or Simvastatin (Zocor®)), fibrates (drugs that reduce triglyceride levels (e.g., Gemfibrozil (Lopid®) or Fenofibrate (Antara®, Fenoglide®, Lipofen®, Lofibra®, Tricor®, Triglide®, Trilipix®)), nicotinic acid (aka niacin), ezetimibe (Zetia®), bile acid sequestrants (e.g., cholestyramine (Locholest®, Prevalite®, Questran®), colestipol (Colestid®), colesevelam (WelChol®), and alirocumab (e.g., (Praluent®) and evolocumab (Repatha®), which are included in a new class of drugs called proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitors as alternative to statin treatments).

[0176] In embodiments where heart failure is detected or risk thereof, the subject may be administered a therapeutically effective amount of a standard-of-care medication, including, or example: (1) an angiotensin-converting enzyme (ACE) inhibitor (e.g., Captopril (Capoten®), Enalapril (Vasotec®), Fosinopril (Monopril®), Lisinopril (Prinivil®, Zestril®), Perindopril (Aceon®), Quinapril (Accupril®), Ramipril (Altace®), or Trandolapril (Mavik®)), (2) angiotensin II receptor blockers or inhibitors (e.g., Candesartan (Atacand®), Losartan

(Cozaar®), Valsartan (Diovan®)), If channel blocker or inhibitor (e.g., Ivabradine (Corlanor®)), beta blockers (e.g., Bisoprolol (Zebeta®), Metoprolol succinate (Toprol XL®), Carvedilol (Coreg®), Carvedilol CR (Coreg CR®)Toprol XL), aldosterone antagonists (e.g., spironolactone (Aldactone®), Eplerenone (Inspra®)), diuretics (also known as water pills) (e.g., Furosemide (Lasix®), Bumetanide (Bumex®), Torsemide (Demadex®), Chlorothiazide (Diuril®),

Amiloride (Midamor Chlorthalidone (Hygroton®)), Hydrochlorothiazide or HCTZ (Esidrix, Hydrodiuril®), Indapamide (Lozol®), Metolazone (Zaroxolyn®), or Triamterene (Dyrenium®).

[0177] In embodiments relating to the detection of chronic kidney disease, the skilled person will appreciate that there are no known cures; however, the complications arising from chronic kidney disease can be controlled by standard-of-care treatments that include: (1) high blood pressure medications; (2) medications to lower cholesterol levels; (3) medications to treat anemia; (4) medications to relieve swelling; (5) medications to protect bones; and (6) lower protein diet to minimize waste products in the blood.

[0178] The term“inhibitors, activators, or modulators” of the target molecules are used to refer to activating, inhibitory, or modulating molecules identified using in vitro and in vivo assays. Inhibitors are compounds that, e.g., bind to, partially or totally block activity, decrease, prevent, delay activation, inactivate, desensitize, or down regulate the activity or expression of target molecules. "Activators" are compounds that increase, open, activate, facilitate, enhance activation, sensitize, agonize, or up regulate activity of target molecules, e.g., agonists.

Inhibitors, activators, or modulators also include genetically modified versions of target molecules, e.g., versions with altered activity, as well as naturally occurring and synthetic ligands, antagonists, agonists, antibodies, proteins, fusion proteins, peptides, cyclic peptides, polynucleotides, oligonucleotides, antisense molecules, ribozymes, RNAi molecules, aptamers, sugars, polysaccharides, lipids, fatty acids, small organic molecules, small chemical compounds, dendrimers, nanovesicles, microvesicles and the combinations of any of these. Such assays for inhibitors and activators include, e.g., expressing target molecules in vitro, in cells, or cell extracts, applying putative modulator compounds, and then determining the functional effects on activity, as described above.

[0179] According to the methods described herein, a subject may be determined to be at risk of developing a condition associated with pre-eclampsia, such as a cardiovascular condition (e.g., hypertension, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia) or a chronic kidney condition (e.g., chronic kidney disease (CKD)). In situations where the pre eclampsia condition is only predicted to occur with a higher risk at a future point in time, and does not actually exist at the time of diagnosis, a subject can also take action by making various lifestyle modification which are aimed to reduce the diagnosed risk.

[0180] In one approach, a subject who is diagnosed with an increased risk of developing at pre eclampsia associated condition later in life could follow the guidelines of the American Heart Association’s Life’s Simple 7® checklist, which involves (a) controlling cholesterol; (b) reducing blood sugar; (c) increasing level/frequency of physical activity; (d) eating a healthier diet; (e) losing weight; and (f) ceasing smoking.

[0181] In another approach, a subject who is diagnosed with an increased risk of developing at pre-eclampsia associated condition later in life could follow the guidelines of the Center for Disease Control, which advises (a) limiting tobacco use; (b) limiting high blood pressure; (c) limiting high blood cholesterol; (d) regulating type 2 diabetes; (e) eating a healthier diet; (f) avoiding being overweight; and (g) increasing level/frequency of physical activity.

[0182] Subjects and their doctors can follow the standard-of-care guidelines developed by the American Heart Association, the CDC, or the American College of Obstetricians and

Gynecologists, or other similar medical and government bodies, to make various lifestyle changes that would tend to counteract the long term effects of the pre-eclampsia associated condition. For example, the American College of Obstetricians and Gynecologists recommends after pre-eclampsia a yearly assessment to check blood pressure, cholesterol, weight, and blood sugar levels for women with a history of early onset or recurrent pre-eclampsia. [0183] Without further elaboration, it is believed that one skilled in the art can, based on the above description, utilize the present invention to its fullest extent. The following specific embodiments are, therefore, to be construed as merely illustrative, and not limitative of the remainder of the disclosure in any way whatsoever. All publications cited herein are incorporated by reference for the purposes or subject matter referenced herein.

[0184] Any state of the art method may also be to verify results from the instantly described predicative method.

[0185] For example, protein in the urine (proteinuria) was once considered a diagnostic sign of pre-eclampsia. However, not all women with pre-eclampsia will have proteinuria. The state of the art no longer considers proteinuria as a necessary sign for diagnosing pre-eclampsia. Now, a healthcare practitioner will look for high blood pressure along with proteinuria, or high blood pressure plus one of a number of other signs and symptoms, including a low platelet count, poor kidney function, poor liver function, severe changes in vision, or edema.

[0186] The following tests are currently used to diagnose pre-eclampsia, determine its severity, and/or monitor its progression. The following well-known tests can be used in conjuction with, or to verify the results of the intant method:

• Urine protein and urine protein to creatinine ratio - used to look for elevated protein in the urine;

• BUN, serum creatinine, and uric acid - kidney function tests used to look for organ damage resulting from pre-eclampsia; serum creatinine will be measured frequently to monitor your condition;

• Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) - elevated levels of these liver function tests may indicate organ damage from pre-eclampsia;

• ALT and AST will be measured frequently to monitor your condition;

• Complete blood count (CBC) - ordered to look for changes in the blood associated with pre-eclampsia, such as low platelet counts;

• Partial thromboplastin time (PTT) - used to measure the time it takes for a person's blood to clot; PTT may be prolonged because pre-eclampsia can extend blood clotting times;

• Antiphospholipid antibodies - Antiphospholipid syndrome is an autoimmune disorder associated with pre-eclampsia and other pregnancy complications. Tests for these antibodies can determine if an autoimmune disorder is underlying your pre-eclampsia; • Peripheral blood smear - red blood cells are examined with a microscope for damage or abnormalities;

• Serum lactate dehydrogenase (LD) - elevated LD levels indicate tissue or cell damage, as occurs in the breakdown of red blood cells;

• Total bilirubin - elevated levels of bilirubin are an indication of liver damage or red blood cell hemolysis.

EXAMPLES

[0187] In order that the invention described herein may be more fully understood, the following examples are set forth. The examples described in this application are offered to illustrate the methods, compositions, and systems provided herein and are not to be construed in any way as limiting their scope.

Example 1: Differentially methylated regions (DMRs) implicated in immune response, vascular regulation, cell invasion, decidualization, as well as systemic regulation of blood pressure, kidney function, and vascular response

Background

[0188] Pre-eclampsia is a syndrome that is mainly considered as the development of new-onset hypertension in the second half of pregnancy. Although frequently accompanied by new-onset proteinuria, pre-eclampsia can be connected with many other marks and symptoms, including visual disturbances, cephalalgia, epigastric pain, and the fast evolution of edema. Hypertension is generally thought to be present when the patient is diagnosed with a persistent systolic blood pressure (BP) of 140 mm Hg or higher or a diastolic BP of 90 mm Hg or higher after 20 weeks of pregnancy in a women with previously normal blood pressure. That said, hypertension alone does not mean that a patient has pre-eclampsia; other criteria are generallyu needed to establish the occurrence of pre-eclampsia. In some cases, these other criteria include new-onset proteinuria, new-onset thrombocytopenia, impaired liver function, renal insufficiency, pulmonary edema, or visual or cerebral disturbances.

[0189] This disorder is relatively frequent, occurring in 5%-8% of pregnancies, and is the third leading cause of maternal mortality in the US. The common pathophysiology of pre-eclampsia is generally believed to result from: (1) vasoconstriction with exaggerated response to vasoactive substances; (2) plasma volume reduction due to capillary leakage and redistribution and shift of the extracellular volume from the intravascular to the interstitial compartments; and (3) platelet aggregation triggered by endothelial dysfunction which leads to intravascular thrombosis. These three factors cause reduced perfusion of the brain, liver, kidneys, and the utero-placental complex reflected in the clinical syndrome.

[0190] According to the new American College of Obstetrician and Gynecologists (ACOG) guidelines, the diagnosis of pre-eclampsia no longer requires the detection of high levels of protein in the urine (proteinuria). Evidence shows organ problems with the kidneys and livers can occur without signs of protein, and that the amount of protein in the urine does not predict how severely the disease will progress. Prior to this time, most healthcare providers traditionally adhered to a rigid diagnosis of pre-eclampsia based on blood pressure and protein in the urine(proteinuria).

[0191] Various clinical trials have helped guide the management of several aspects of pre eclampsia but have fallen short of providing a comprehensive picture for treating the condition. For example, reviews of maternal mortality data show that deaths could be avoided if health care providers remain alert to the likelihood that pre-eclampsia will progress. In addition,

intervention in acutely ill women with multiple organ dysfunction is sometimes delayed because of the absence of proteinuria. Furthermore, accumulating information indicates that the amount of proteinuria does not predict maternal or fetal outcome. Thus, alternative diagnostic methodologies are needed to improve the diagnosis of pre-eclampsia and to better predict maternal and/or fetal outcomes.

[0192] The present method provides low-pass epigenetic analysis of blood DNA samples (cell- free DNA) as an alternative approach for diagnosing and managing the clinical treatment of pre eclampsia. The method provides an“epigenetic memory” that reflects a multi-organ

involvement of pre-eclampsia and which is associated with an increased risk of future cardiovascular complications. The method establishes that epigenetic characteristics detected in cfDNA (aka cfDNA methylomes) reveal epigenetic patterns that are linked to individual patient pathologies, which can be leveraged to improve the diagnosis, treatment and prevention of pre eclampsia in women.

[0193] The method provides an acurate diagnosis of pre-eclampsia and also includes penetrance (i.e., the proportion of people with a particular genetic change (such as a mutation in a specific gene) who exhibit signs and symptoms of a genetic disorder) of comorbidities thereby improving both diagnosis and treatment. This method is useful in routine pregnancy monitoring. The method may also be implemented more regularly in connection with pregnant women who are at risk of pre-eclampsia. The test can be conducted by clinical laboratories using standard laboratory tests.

Overview of study

[0194] Clinical applications of cell-free DNA (cfDNA) analysis are limited to targeted and/or disease-specific applications. The utility of rapid and automatable cfDNA low-pass methyl-CpG binding domain sequencing was investigated in assessing severe pre-eclampsia (sPE), a pregnancy disorder characterized by hypertension and proteinuria associated with vascular resistance dysfunction. DNA methylation profiles were compared between a cohort of sPE patients and healthy matched controls. sPE patients presented an increase in cfDNA, reduced fetal fraction and increase maternal cfDNA derived from kidney and smooth muscle cells, and less from placenta origin. The data showed modifications in placenta- and decidua-specific differentially methylated regions (DMRs) implicated in immune response, vascular regulation, cell invasion and decidualization. DMRs potentially implicated in the systemic regulation of blood pressure, kidney function and vascular response were also identified. The results highlights the existence of epigenetic modifications originated in pre-eclampsia that may be of importance in the increased risk for cardiovascular and chronic kidney disease later in life.

Cell-free DNA

[0195] The present Example evaluates for the presence of methylation markers in circulating cell-free DNA (cfDNA). Circulating cell-free DNA (cfDNA) comprises DNA fragments of ~ 180 bp present in plasma and originating mainly from apoptotic cells (7). The mean half-life for circulating fetal DNA was 16.3 min (range 4-30 min) (2). Measurement of cfDNA is being tested and used as a novel analyte for early diagnosis and disease monitoring (7). These DNA fragments carry epigenetic features such as DNA methylation and histone modifications (3). Changes in methylated cfDNA have been found in plasma from patients with different pathologies ( 4 , 5), as well as during normal pregnancy and pathological conditions such as molar pregnancy (6). Further, each tissue has a specific methylation pattern (7, 5), indicating that epigenetic marks could identify the tissue of origin of cfDNA (9, 10). Thus, cfDNA methylation profiling may provide insight into tissue-specific damage in systemic diseases and enable understanding of whether epigenetic memory contributes to long-term morbidity associated with pathological conditions.

[0196] Several studies have investigated cfDNA in PE maternal blood, finding an increased concentration in pre-eclamptic patients versus normal pregnancies (73, 14). On the other hand, studies of DNA methylation in PE have focused on the analysis of placental tissue after delivery, identifying significant methylation differences compared with control pregnancies (6, 15-21). These studies, however, suggest that identifying DNA methylation changes in cfDNA in PE, a multi-system disease model, could enable the discovery of systemic epigenetic changes associated with the disease. Such changes may originate from organs affected by the disease and represent potential tools to assess tissue damage or follow-up of long-term clinical

complications.

[0197] Here, in various aspects, low-pass methyl-CpG binding-domain sequencing (MBD-seq) was used due to the low concentration of cfDNA available, because, unlike sodium bisulfite treatment, it does not produce DNA degradation. MBD-seq offers a comprehensive first pass at the CpG methylome and affordable with the samples sizes required for MWAS (22). Standard molecular and bioinformatics tools were used to investigate cfDNA methylomes in patients with severe PE (sPE) to identify methylation changes that could potentially indicate systemic responses to this condition, with potential implications for the long-term effects of the disease on CVD and CKD risk.

Materials and Methods

Subjects, blood processing and DNA extraction

[0198] Patients and controls were recruited from Hospital La Fe after written informed consent was provided for all donors. The study was approved by the Clinical Research Ethics Committee of Hospital La Fe (Valencia, Spain). Blood samples were collected from patients with severe preeclampsia (sPE; n=12) clinically diagnosed based on a further elevation of blood pressure (systolic pressure >160 mm Hg or diastolic pressure >110 mm Hg) or any of the following: thrombocytopenia, impaired liver function, progressive renal insufficiency, edema, or the new onset of cerebral or visual disturbances. Patients who had normal obstetric outcomes with gestational age matched with sPE cases were selected as controls (C; n=12). The clinical features of the participants are summarized in Table 8.

[0199] Table 8

Mean +/- standard error of the mean (SEM); *One-tailed Student^'s t-test

NA: Not available

[0200] Peripheral blood samples (10 mL) were collected into cell-free DNA BCT collection tubes (Streck, NE, USA) and were centrifuged 10 min at 1900 rpm at 4°C to separate plasma. Circulating cell-free DNA was extracted from 1 mL of plasma using QIAsymphony DSP Virus/Pathogen Kit (Qiagen, Germany) according to the manufacturer's instructions.

Methylated DNA enrichment

[0201] MethylMiner™ Methylated DNA Enrichment Kit (Invitrogen, CA, USA) was used to isolate methylated DNA from 50 pL of extracted cfDNA. Methyl-binding protein was coupled to beads following kit instructions, but instead of manual specified volumes, 2.2 pL Dynabeads^® and 1.5 pL MBD-biotin protein per sample were used. After incubation with cfDNA, step-wise elution was performed with 160 mM NaCl, 450 mM NaCl and 2000 mM NaCl buffers. Then, DNA from the third (highly-methylated DNA) step was cleaned-up with AMPure beads (Agencourt) and eluted in a final volume of 50 pL (FIG. 5).

Library preparation and sequencing

[0202] Libraries were prepared from each sample’s methylated (highly-methylated) fraction by performing end repair, mono-adenylation, adapter ligation and PCR amplification (TruSeq Nano DNA Library Prep Kit, Illumina). After purification, library concentration was quantified using a fluorometry-based method (Qubit High Sensitivity dsDNA kit; Life Technologies). Size distribution of the library DNA was assessed using a 2100 Bioanalyzer and high-sensitivity DNA chips (Agilent). Normalized samples were pooled, and single-end sequencing was performed for 150 cycles for methylated samples in NextSeq 500 sequencing system (Illumina).

Data analysis

Pre-processing and quality controls

[0203] Quality control checks and %GC content calculations were carried out using FastQC (bioinformatics.bbsrc.ac.uk/projects/fastqc). Sequences were mapped to the Hgl9 version of the human genome using BWA (55), allowing for up to 3 mismatches. Uniquely aligned reads were used in subsequent analyses.

Sample BED files

[0204] Bam files from each sample were used to obtain BED files for normalized, filter-passed window count values. Methylaction software maTracks function (26) was used to obtain the normalized coverage files for each sample.

Placental cell free concentration estimation

[0205] Data from methylated library sequencing was used to estimate fetal fraction. For pregnancies carrying a male fetus (C, n=5; sPE, n=5), mean coverage was calculated for chromosome Y in the resulting normalized coverage files. Mean coverage of regions

corresponding to defined epigenetic placental markers RASSF1A and SERPINB5 (23, 25) (24) was calculated for all the samples using normalized coverage files.

Assessment of circulating cfDNA tissue of origin

[0206] With the aim of assessing cfDNA tissue of origin, data from BED files of methylated DNA library sequences were compared with reduced representation bisulfite seq (RRBS) data collected from ENCODE (hgl9) (54). References for adrenal gland, aortic smooth muscle cells, brain, kidney, left ventricle, leukocyte, liver, pancreas, placenta, skeletal muscle and uterus were selected. Different replicates available from the same tissue were downloaded and joined to have one single reference for each tissue. Mean read count and mean methylation percentage was calculated for each tissue reference and only those regions with values greater than the mean were used for subsequent analysis. Because RRBS data are made of single positions BEDTools (bedtools.readthedocs.io/) (55) were used to join those that were 350 bp or closer. Only regions that come from the union of 4 or more regions were kept for subsequent analysis (FIG. 7A).

Each tissue was compared with the rest and specific-tissue regions were obtained (FIG. 7B) (BEDTools intersect -v). After that, each tissue reference was compared with BED files resultant from Methylaction from each participant’s cfDNA methylated data. Regions in the tissue that overlapped with participant BED regions were obtained. With the resultant BED files, mean coverage of each tissue regions was calculated for each group.

Differentially methylated regions (DMRs)

[0207] Differentially methylated regions (DMRs) between control and severe preeclampsia were detected using methylated DNA data with Methylaction software

(github.com/ieffbhasin/methylaction) (26). A window size of 50 bp and a fragment size of 375 bp were selected based on library size; chromosomes X and Y were excluded from the analysis.

[0208] Annotation of DMRs with respect to features and genomic regions was performed using the package Goldmine in Bioconductor (github . com/j effbhasin/ goldmine) (27). DMRs obtained with Methylaction were annotated to GRCh37/hgl9 human genome assembly, and resulting data contained genomic context annotation (promoter, exon, intron, 3’ end, intergenic) and gene annotation (UCSC knowhnGene table). Feature data were obtained from the UCSC Table Browser. TFBS (‘wgEncodeRegTfbsClusteredV3’) and CpG (“CpglslandsExt”) data were used to annotate DMRs.

Pathways enrichment

[0209] A subset of DMRs that annotated as promoters was generated for each group. Annotated genes for these regions were compared between groups, and those that appeared in both subsets were removed. For detection of significantly over-represented pathways and diseases, The Database for Annotation, Visualization and Integrated Discovery v6.8 (DAVID,

david.ncifcrf.gov/home.jsp) was used. Functional annotation clustering tool was performed for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Genetic Association Database (GAD) (for diseases related analysis) using custom parameters (Similarity Term Overlap=4, Similarity Threshold=0.35, Initial Group Membership=3, Final Group

Membership=3, Multiple Linkage Threshold=0.5 and EASE=0.055).

Functional analysis of DMRs annotated to TFBS

[0210] DMRs that annotated to TFBS were selected and overlap was computed with GO gene sets of the Molecular Signatures Database v6.1 (56) with GSEA software

(software.broadinstitute.org/gsea/index.jsp). Top 10 genes with false discovery rate (FDR) q- value below 0.05 were obtained for both TFBS annotated regions in hmsPE and hmControl. Differentially methylated regions and tissues

[0211] DMRs were compared to tissue-specific references using BEDTools (intersect -v) to obtain DMRs that overlapped with tissue-methylated regions. Results

Circulating methylated cfDNA concentration increases in sPE pregnancies

[0212] cfDNA was extracted and analyzed from plasma of patients diagnosed with sPE (n=12) at a mean of 32.9 weeks of pregnancy and from controls (C; n=12) with normal obstetric outcome at similar gestational age (32.1 weeks) (Table 8). In both groups, sample collection was obtained approximately one week before delivery. Methyl-CpG binding domain-based (MBD) capture was performed and the resultant methylated fraction underwent library preparation and sequencing (FIG. 5). cfDNA library concentration was significantly higher in sPE compared to controls (sPE: 2.569 ng/mL vs C: 1.236 ng/pL, p=0.0386) (FIG. 1A). The size distribution of cfDNA was similar in both sPE and control groups, and the majority of cfDNA fragments fell within sizes corresponding to one- and two-nucleosome fragments (FIG. IB).

[0213] To identify whether the increased levels of methylated cfDNA in sPE originated from placenta, the following strategies were used. First, the mean coverage of Y chromosome in pregnancies with a male fetus (n=5/per group) was quantified, resulting in lower Y chromosome mean coverage in the sPE group (FIG. 1C) (C: 5.016; sPE: 3.997, p=0.0952). This analysis was extended to the placental hypermethylated marker RASSF1A (23, 24); a significantly lower value was observed in the sPE group (FIG. ID) (C: 2.5378 sPE: 1.60, p=0.0001). SERPINB5, a hypomethylated placental marker (25), had significantly higher coverage in the sPE group (FIG. IE) (C: 5.25 sPE: 8.02, p= 0.0267), indicative of reduced fetal fraction. Therefore, after ruling out a differential placental contribution, these data suggest that the quantitative increase in methylated cfDNA in sPE patients might originate from maternal organs in response to this pathological condition.

Organ/tissue contribution to circulating methylated cfDNA in sPE

[0214] To investigate the systemic contribution of different organs and tissues to circulating cfDNA, data was analyzed from specific organ methylation signatures. Reduced bisulfite representation sequencing (RRBS) data was used, generated by the ENCODE consortium as the basis for this comparison. Since these data are defined at a single base position level, an “expanded window” was generated by extending the original marker site to their adjacent regions to be able to compare these data with the MBD-seq data. To compute the different contribution of each tissue to methylated cfDNA between sPE and control, data from nine different organs were compared to sequencing data from each patient (n=12 per group). Briefly, the mean coverage of tissue regions was compared between groups (see Methods). The results give an estimation of tissue contribution to the methylated cfDNA in control and sPE pregnancies (FIG. IF). Vascular smooth muscle cell (SMC, mean coverage: sPE 7.06; control 6.48), and kidney (mean coverage: sPE 7.47; control 7.18) origins were significantly higher in the sPE group (SMC p= 0.005; Kidney p=0.05), while placental (mean coverage: sPE 2.77; control 3.15. p< 0.0001) and skeletal muscle cell (mean coverage: sPE 5.59; control 5.94. p< 0.0001) origins were significantly lower (FIG. 1G). Placental weight was significantly reduced in sPE compared with control pregnancies, correlating with lower placental tissue contribution (FIG. 1H).

Unsupervised analysis of differentially methylated regions (DMRs) in sPE

[0215] The Methylaction software package (26) was used to investigate differentially methylated regions (DMRs) in sPE and control pregnancies. Analysis identified a total of 228,468 DMRs (anodev.p-adj < 0.05), including 40,409 hypermethylated regions in sPE (hmsPE) and 188,059 hypermethylated regions in the control group (hmControl). DMRs were annotated with Goldmine software (27) depending on their genomic and CpG context. hmsPE and hmControl DMRs were found in all analyzed genomic contexts (3 ^' end, exon, intergenic, intron and promoter) (FIG. 2A). The most represented regions in both groups were intronic and intergenic, and the smallest percentage corresponded to exonic regions. The main difference between groups was observed in regions annotated as promoters; the proportion of those was significantly higher in sPE (18.7% in hmsPE vs 7.9% in hmControl, p < 2.2e-16).

[0216] DMRs were also annotated regarding their CpG context (FIG. 2B). In total 18,497 hmControl and 20,671 hmsPE overlapped with CpG regions; the proportion of hypermethylated regions that annotated to CpG context was significantly higher in sPE (0.09% hmControl, 5.11% hmsPE; p< 2.2e-16). Significant differences were found in the distribution of the regions between groups. The proportion of CpG shelves was significantly lower in sPE (C: 50.1%, sPE: 17.7%, p<2.2e-16), while the proportions of CpG shores and CpG islands were significantly higher in sPE (CpG shores C: 48.7%, sPE: 53.8%, p<2.2E-16; CpG islands C: 1.2%; sPE:

28.4%; p<2.2e-16).

[0217] To study the potential role of methylation changes, DMRs annotated as promoters were selected to perform gene functional classification analysis. Pathway enrichment analysis revealed that hmsPE annotated in promoter regions were significantly associated with KEGG pathways (FIG. 2C) such as B and T cell receptor signaling and leukocyte transendothelial migration, insulin resistance, insulin, adipocyte and AMPK signaling pathways.

[0218] DNA methylation can affect transcription factor binding and/or activity, modulating the expression of target genes (28) Therefore, all DMRs that annotated to transcription factor binding sites (TFBS) were comparatively investigated. Interestingly, 23 and 28 TFBS were over-represented (fold change >2) in sPE and controls, respectively (FIGs. 6A-6B). Gene ontology was assessed using Gene Set Enrichment Analysis (GSEA), which showed that 23 TFBS hyperm ethylated in the sPE group were enriched in negative regulators of transcription and gene expression, as well as in chromatin modification and organization factors (FIG. 2D). In contrast, in the control group, TFBS mapped to regions associated with positive regulation of gene expression (FIG. 2E).

Identification of DMRs with potential implication in sPE long-term morbidity

[0219] To identify methylation changes that could be indicators of sPE pathology, disease associations were explored using the Genetic Association Database in the DAVID online tool (36). Genes corresponding to DMRs that annotated to promoters with fold change >2 were selected. In the sPE group, in addition to pre-eclampsia involving 19 genes (0.7%, p= 4.9e-02), the most represented diseases were type 2 diabetes, (452 genes, 17.4%, p=1.8e-15), chronic renal failure (178 genes, 6.9%, 4.2e-05) and hypertension (139 genes, 5.4%, p= 2.9e-04). Liver metabolic dysfunction, HDL cholesterol (HDL-C) levels (64 genes, 2.5%, p=9.6e-06) and metabolic syndrome (37 genes, 1.4%, p=1.0e-02) were also significantly represented (Table 9). No relevant disease annotation was obtained in hmControl regions identified as promoters.

[0220] To search specifically for changes related to the control of blood pressure in sPE patients, 24 genes involved in the renin-angiotensin-aldosterone system (57) (RAAS) were explored (AGT, ACE, ACE2, AGTR1, AGTR2, AGTRAP, ATP6AP2, CMA1, CYP11B2, ENPEP, ERAP1, LNPEP, MAS1, MME, MRGPRD, NEDD4L, NOS3, PRCP, REN, SCNN1A, SCNN1B, SCNN1D, SCNN1G and STK39). Two genes appeared hypermethylated in hmControl annotated as promoters: aldosterone synthase ( CYP11B2 ) and STK39. Among the hmsPE promoter regions, it was found that SCNN1A and SCNN1D , two subunits of the epithelial sodium channel, were hypermethylated in the sPE group. NEDD4L , ACE and PRCP were also in hmsPE annotated as promoters (FIG. 4A). In addition, endothelial nitric oxide synthase ( NOS3 ), a key regulator of vascular function, had a total of 7 DMRs (FIG. 4B). Two regions, DMR1 and DMR7, appeared hypermethylated in the sPE group; specifically, DMR7 was annotated as a promoter region and had several potential TFBS.

[0221] Finally, a functional relationship with blood pressure regulation was explored.

Interestingly, a negative correlation between methylation level in these regions and blood pressure was observed (FIG. 4C). The lower the systolic and diastolic blood pressures were, the higher the observed methylation levels.

[0222] In summary, the data illustrate the multi-organ pathology of PE and identify epigenetic changes that could be implicated in long-term morbidity associated with this condition, such as vascular and kidney functional alterations, by the perpetuation of DNA methylation changes.

Discussion

[0223] The work demonstrates the potential utility of identifying methylation changes in cfDNA for disease characterization and monitoring. For this purpose, MBD capture and low-pass sequencing, standard analysis, and data available in public databases were used. sPE was studied as an optimal multi-system disease to test this method in the identification of systemic and pathological alterations in disease. As previously reported [reviewed in (55)], the existence of a significant increase in cfDNA library concentration in patients with sPE compared to controls (2-fold increase) was corroborated. However, it was demonstrated by different approaches that this increase cannot be attributed to the placental/fetal fraction, in agreement with results reported by Rolnik et al (39) and Rafaeli-Yehudai et al (40), who also show that the increase in cfDNA had a maternal origin.

[0224] Using gene-disease ontology on hypermethylated promoters, it is shown that, in addition to pre-eclampsia and kidney injury, women with sPE exhibit a methylation signature of metabolic syndrome, including hypertension, insulin resistance and alteration in lipid profile.

The results identified relevant changes in regulators and effectors of RAAS, the system controlling blood pressure. Promoter regions of PRCP and ACE genes, involved in the degradation and the production of Angll, respectively, were hypermethylated in the sPE group. Moreover, PRCP converts Angll to Ang (1-7), and its deficiency has been associated with a rise in blood pressure {41). In addition, hypermethylation of NOS3 in sPE patients may affect their vascular function. Blocking the production of NO induces the majority of symptoms of PE in pregnant mice and rats ( 42 ), and genetic variations in the NOS3 gene contribute to an increased risk for PE (43). Potential targets in regulation of the epithelial sodium channel (ENaC) were also found; aldosterone synthase gene ( CYP11B2 ) and STK39 promoter regions are hypermethylated in control group, while NEDD4L , which participates in the degradation of ENaC, is

hypermethylated in sPE. Two of the subunits of this channel, SCNN1A and SCCN1D , were hypermethylated in sPE. ENaC expression has been previously reported to be reduced in sPE placentas and associated with compromised trophoblastic invasion (44).

[0225] Among the DMRs that corresponded to placenta-specific tissue regions, genes that have been previously related with preeclampsia were found, such as MMP25 , which has lower expression in pre-eclamptic placentas (45), and CRHBP, which has increased methylation in placentas from sPE patients (46). Interesting links with the NO pathway were also found.

GUCY1B3 is the major receptor of NO in the vascular wall and a key enzyme in the NO signaling pathway, participating in the vasodilatation process and its expression is affected in hypertension (47). Further, the promoters of genes relevant for the matemo-fetal interaction, like ALCAM (29) and GDF10 (30), and genes implicated in decidualization, regulating trophoblast invasion and differentiation, such as TLX1 (31), HOXA9 (32), CD38 (33), LPL (34) and NKX2.5 (35), also had DMRs. These genes have been previously associated with defective endometrial decidualization detected at the time of delivery and 3 to 4 years after the sPE (35, 48).

[0226] Notably, it must be considered that it is not possible to rule out that some DMRs may have originated from different tissues. However, the identification of the all PE pathological features by gene-disease ontology strengthens the validity of the approach.

[0227] DNA methylation patterns are transmitted during DNA replication (49). This cellular epigenetic memory integrates the information of the local chromatin environment. Perpetuation of DNA methylation patterns is critical for retaining cellular identity after cell division, which is necessary for maintenance of the phenotype of differentiated cells. Perpetuation of changes incurred during a past disease can potentially have a role in future pathological situations. In fact, multiple clinical studies of women with PE show an increased risk of developing CVD and CKD later in life (12, 50). Therefore, methylation changes relevant for disease pathology could be linked to future sequelae. sPE is a placental disease and the data indicate that, even though placental cfDNA is not increased in the plasma of pre-eclamptic patients, it is the most epigenetically-altered organ in the analysis. Further, although PE has a placental origin, it affects both mother and fetus. After delivery, some of these alterations epigenetic changes can have future consequences in maternal health by perpetuation of epigenetic modifications. Future work is needed to identify potential targets that may help prevent later-life pathologies associated to past PE.

[0228] In summary, the potential of low-pass MBD-seq is presented as a practical strategy for disease-associated DMR discovery. Using data available in public databases and standard bioinformatic tools relevant data was obtained regarding the multi-organ involvement in sPE that can potentially produce an epigenetic memory that increases risk for future cardiovascular complications. In fact, women who have had a pre-eclamptic pregnancy show reduced endothelium-dependent vasodilation and increased sensitivity to Angll, reduced endothelium- dependent dilation mediated by NO (57) and endothelial dysfunction (52). Finally, this research highlights the potential utility of this simple method in other diseases. Additional research is warranted to establish cfDNA methylomes in both control and patient data to reveal patterns linked to individual patient pathology, to ultimately improve diagnosis, treatment and prevention.

REFERENCES

1. J. Aucamp, A. J. Bronkhorst, C. P. S. Badenhorst, P. J. Pretorius, The diverse origins of circulating cell-free DNA in the human body: a critical re-evaluation of the literature. Biological reviews of the Cambridge Philosophical Society 93, 1649-1683 (2018).

2. Y. M. Lo et al., Rapid clearance of fetal DNA from maternal plasma. American journal of human genetics 64, 218-224 (1999).

3. R. H. Xu, B. et al., Circulating tumour DNA methylation markers for diagnosis and prognosisEpigenetic modifications at DMRs of hepatocellular carcinomaplacental genes are subjected to variations in normal gestation, pathological conditions and folate supplementation. Nat MaterSci Rep 167, 1155-116140774 (2017). 4. R. W. Chan et al., Plasma DNA aberrations in systemic lupus erythematosus revealed by genomic and methylomic sequencing. Proc Natl Acad Sci U S A 111, E5302-5311 (2014).

5. M. Fleischhacker, B. Schmidt, Circulating nucleic acids (CNAs) and cancer— a survey. Biochim Biophys Acta 1775, 181-232 (2007).

6. B. Rahat, A. Mahajan, R. Bagga, A. Hamid, J. Kaur, Epigenetic modifications at DMRs of placental genes are subjected to variations in normal gestation, pathological conditions and folate supplementation. Sci Rep 7, 40774 (2017).

7. A. F. Fernandez et al., A DNA methylation fingerprint of 1628 human samples. Genome Res 22, 407-419 (2012).

8. A. Kundaje et al, Integrative analysis of 111 reference human epigenomes. Nature 518, 317-330 (2015).

9. K. Sun et al., Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci U S A 112, E5503-5512 (2015).

10. R. Lehmann- Werman et al., Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci U S A 113, E1826-1834 (2016).

11. L. Bellamy, J. P. Casas, A. D. Hingorani, D. J. Williams, Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. BMJ 335, 974 (2007).

12. A. M. Hildebrand, M. A. Hladunewich, A. X. Garg, Preeclampsia and the Long-term Risk of Kidney Failure. Am J Kidney Dis 69, 487-488 (2017).

13. M. S. Alberry et al, Quantification of cell free fetal DNA in maternal plasma in normal pregnancies and in pregnancies with placental dysfunction. Am J Obstet Gynecol 200, 98.e91-96 (2009).

14. R. Munoz-Hernandez et al., Total and Fetal Circulating Cell-Free DNA, Angiogenic, and Antiangiogenic Factors in Preeclampsia and HELLP Syndrome. Am J Hypertens 30, 673-682 (2017).

15. S. L. Wilson et al, Placental DNA methylation at term reflects maternal serum levels of INHA and FN1, but not PAPPA, early in pregnancy. BMC Med Genet 16, 111 (2015).

16. K. R. Yeung et al., DNA methylation profiles in preeclampsia and healthy control placentas. Am J Physiol Heart Circ Physiol 310, H1295-1303 (2016). 17. A. Kulkarni, P. Chavan-Gautam, S. Mehendale, H. Yadav, S. Joshi, Global DNA methylation patterns in placenta and its association with maternal hypertension in pre-eclampsia. DNA Cell Biol 30, 79-84 (2011).

18. J. D. Blair et al, Widespread DNA hypomethylation at gene enhancer regions in placentas associated with early-onset pre-eclampsia. Mol Hum Reprod 19, 697-708 (2013).

19. T. Chu et al., Comprehensive analysis of preeclampsia-associated DNA methylation in the placenta. PLoS One 9, el07318 (2014).

20. J. Xuan et al., Comprehensive analysis of DNA methylation and gene expression of placental tissue in preeclampsia patients. Hypertens Pregnancy 35, 129-138 (2016).

21. L. Anton, A. G. Brown, M. S. Bartolomei, M. A. Elovitz, Differential methylation of genes associated with cell adhesion in preeclamptic placentas. PLoS One 9, el00148 (2014).

22. D. Serre, B. H. Lee, A. H. Ting, MBD-isolated Genome Sequencing provides a high- throughput and comprehensive survey of DNA methylation in the human genome. Nucleic acids research 38, 391-399 (2010).

23. K. C. Chan et al., Hyperm ethylated RASSF1 A in maternal plasma: A universal fetal DNA marker that improves the reliability of noninvasive prenatal diagnosis. Clin Chem 52, 2211-2218 (2006).

24. I. Iglesias-Platas, A. Martin Trujillo, F. Court, D. Monk, Distinct promoter methylation and isoform-specific expression of RASFF1A in placental biopsies from complicated

pregnancies. Placenta 36, 397-402 (2015).

25. S. S. Chim et al., Detection of the placental epigenetic signature of the maspin gene in maternal plasma. Proc Natl Acad Sci U S A 102, 14753-14758 (2005).

26. J. M. Bhasin, B. Hu, A. H. Ting, Methyl Action: detecting differentially methylated regions that distinguish biological subtypes. Nucleic acids research 44, 106-116 (2016).

27. J. M. Bhasin, A. H. Ting, Goldmine integrates information placing genomic ranges into meaningful biological contexts. Nucleic acids research 44, 5550-5556 (2016).

28. Y. Yin et al., Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, (2017).

29. H. Fujiwara et al., Human blastocysts and endometrial epithelial cells express activated leukocyte cell adhesion molecule (ALCAM/CD166). J Clin Endocrinol Metab 88, 3437-3443 (2003). 30. M. A. Rahman et al., Hoxa-10 deficiency alters region-specific gene expression and perturbs differentiation of natural killer cells during decidualization. Dev Biol 290, 105-117 (2006).

31. B. Novakovic et al., Increased methylation and decreased expression of homeobox genes TLX1, HOXA10 and DLX5 in human placenta are associated with trophoblast differentiation. Sci Rep 7, 4523 (2017).

32. X. Liu et al, HOXA9 transcriptionally regulates the EPHB4 receptor to modulate trophoblast migration and invasion. Placenta 51, 38-48 (2017).

33. H. N. Ho, K. H. Chao, C. K. Chen, Y. S. Yang, S. C. Huang, Activation status of T and NK cells in the endometrium throughout menstrual cycle and normal and abnormal early pregnancy. Hum Immunol 49, 130-136 (1996).

34. S. Tabano et al., Placental LPL gene expression is increased in severe intrauterine growth-restricted pregnancies. Pediatr Res 59, 250-253 (2006).

35. M. B. Rabaglino, E. D. Post Uiterweer, A. Jeyabalan, W. A. Hogge, K. P. Conrad, Bioinformatics approach reveals evidence for impaired endometrial maturation before and during early pregnancy in women who developed preeclampsia. Hypertension 65, 421-429 (2015).

36. d. W. Huang, B. T. Sherman, R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4, 44-57 (2009).

37. S. E. Thatcher, A Brief Introduction into the Renin-Angiotensin- Aldosterone System: New and Old Techniques. Methods in molecular biology 1614, 1-19 (2017).

38. S. Sifakis, Z. Koukou, D. A. Spandidos, Cell-free fetal DNA and pregnancy-related complications (review). Mol Med Rep 11, 2367-2372 (2015).

39. D. L. Rolnik et al., Maternal plasma cell-free DNA in the prediction of pre-eclampsia. Ultrasound Obstet Gynecol 45, 106-111 (2015).

40. T. Rafaeli-Yehudai et al., Maternal total cell-free DNA in preeclampsia and fetal growth restriction: Evidence of differences in maternal response to abnormal implantation. PLoS One 13, e0200360 (2018).

41. C. Maier et al, Prolylcarboxypeptidase deficiency is associated with increased blood pressure, glomerular lesions, and cardiac dysfunction independent of altered circulating and cardiac angiotensin II. J Mol Med (Berl) 95, 473-486 (2017).

42. F. Zeng et al., Associations between nitric oxide synthase 3 gene polymorphisms and preeclampsia risk: a meta-analysis. Sci Rep 6, 23407 (2016). 43. D. T. Lowe, Nitric oxide dysfunction in the pathophysiology of preeclampsia. Nitric Oxide 4, 441-458 (2000).

44. S. Wang et al., Reduced expression of Enac in Placenta tissues of patients with severe preeclampsia is related to compromised trophoblastic cell migration and invasion during pregnancy. PLoS One 8, e72153 (2013).

45. T. J. Kaitu'u-Lino et al, MT-MMPs in pre-eclamptic placenta: relationship to soluble endoglin production. Placenta 34, 168-173 (2013).

46. K. Hogg, J. D. Blair, D. E. McFadden, P. von Dadelszen, W. P. Robinson, Early onset pre-eclampsia is associated with altered DNA methylation of cortisol-signalling and

steroidogenic genes in the placenta. PLoS One 8, e62969 (2013).

47. C. Rippe et al, Hypertension reduces soluble guanylyl cyclase expression in the mouse aorta via the Notch signaling pathway. Sci Rep 7, 1334 (2017).

48. T. Garrido-Gomez et al, Defective decidualization during and after severe preeclampsia reveals a possible maternal contribution to the etiology. Proc Natl Acad Sci U S A 114, E8468- E8477 (2017).

49. A. V. Probst, E. Dunleavy, G. Almouzni, Epigenetic inheritance during the cell cycle. Nat Rev Mol Cell Biol 10, 192-206 (2009).

50. R. Ahmed, J. Dunford, R. Mehran, S. Robson, V. Kunadian, Pre-eclampsia and future cardiovascular risk among women: a review. J Am Coll Cardiol 63, 1815-1822 (2014).

51. A. E. Stanhewicz, S. Jandu, L. Santhanam, L. M. Alexander, Increased Angiotensin II Sensitivity Contributes to Microvascular Dysfunction in Women Who Have Had Preeclampsia. Hypertension 70, 382-389 (2017).

52. M. Harati-Sadegh, L. Kohan, B. Teimoori, M. Mehrabani, S. Salimi, The association of the placental Hypoxia-inducible factor 1 -a polymorphisms and HIFl-a mRNA expression with preeclampsia. Placenta 67, 31-37 (2018).

53. H. Li, R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009).

54. A. Meissner et al., Genome-scale DNA methylation maps of pluripotent and

differentiated cells. Nature 454, 766-770 (2008).

55. A. R. Quinlan, BEDTools: The Swiss-Army Tool for Genome Feature Analysis. Curr Protoc Bioinformatics 47, 11.12.11-34 (2014). 56. A. Subramanian et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 (2005).

Claims

1. A method for detecting a risk of developing a systemic condition associated with pre eclampsia, comprising: (i) isolating cell-free DNA from a pre-eclampsia patient sample; (ii) analyzing the cell-free DNA for the presence of one or more methylation markers appearing on the cell-free DNA, wherein the one or more methylation markers are predictive of a condition associated with pre-eclampsia.

2. The method of claim 1, wherein the condition is a cardiovascular condition.

3. The method of claim 1, wherein the condition is a kidney condition.

4. The method of claim 1, wherein the step of isolating cell-free DNA comprises methyl- CpG-binding domain-based (MBD) capture.

5. The method of claim 1, wherein the pre-eclampsia patient sample is blood.

6. The method of claim 1, wherein the one or more methylation markers are indicative of a cardiovascular condition.

7. The method of claim 1, wherein the one or more methylation markers are indicative of a kidney condition.

8. The method of claim 1, wherein the one or more methylation markers occurs in the promotor of a gene associated with cardiovascular health.

9. The method of claim 1, wherein the one or more methylation markers occurs in the promotor of a gene associated with kidney health.

10. The method of claim 1, wherein the one or more methylation markers is a differentially methylated region (DMR) of a gene involved in a condition associated with pre-eclampsia.

11. The method of claim 10, wherein the condition associated with pre-eclampsia is a cardiovascular condition.

12. The method of claim 11, wherein the cardiovascular condition is hypertension, stroke, atherosclerosis, heart failure, dilated cardiomyopathy, or heart arrhythmia.

13. The method of claim 1, wherein the one or more methylation markers comprises a differentially methylated region of a gene selected from the group consisting of: SCNN1A, SCNN1D, NEDD4L , ACE , PRCP, NOS3, and which are predictive of a cardiovascular condition.

14. The method of claim 1, wherein the one or more methylation markers comprise a differentially methylated region of a regulator or effector of RAAS , and which are predictive of high blood pressure.

15. The method of claim 1, wherein the one or more methylation markers comprise a differentially methylated region in CYP11B2, STK9, NEDD4L , or SCNN1D, and predictive of high blood pressure.

16. The method of claim 1, wherein the one or more methylation markers comprise a differentially methylated region in ALCAM or GDF10 and which are predictive of the matemo- fetal interaction.

17. The method of claim 1, wherein the one or more methylation markers comprise a differentially methylated region in TLX1, HOXA9, CD38, LPL, and NKX2.5 and which are predictive of defective endometrial decidualization after delivery.

18. The method of claim 1, wherein the step of analyzing the cell-free DNA for the presence of one or more methylation markers comprising sequencing the cell-free DNA.

19. The method of claim 18, wherein the sequencing is by low-pass MBD-seq.

20. The method of claim 1, wherein the step of isolating cell-free DNA comprises methyl- CpG-binding domain-based (MBD) capture.

21. The method of claim 1, wherein the pre-eclampsia patient sample is maternal blood.

22. The method of claim 1, wherein the pre-eclampsia patient sample if a maternal organ or tissue.

23. The method of claim 1, further comprising the step of administering a therapeutically effective amount of an antihypertensive drug where the method detects a risk of hypertension.

24. The method of claim 1, further comprising the step of making a lifestyle modification where the method detects a risk of cardiovascular disease.

25. The method of claim 1, wherein the one or more methylation markers is selected from Tables 2-7, or from any combination of said Tables.

26. The method of claim 24, wherein the lifestyle modification comprises one or more of the following recommendations from the American Heart Association’s Life’s Simple 7® checklist: (a) control cholesterol; (b) reduce blood sugar; (c) increase level/frequency of physical activity; (d) eat a healthier diet; (e) lose weight; and (f) cease smoking.

27. The method of claim 24, wherein the lifestyle modification comprises one or more of the following recommendations from the Center for Disease Control: (a) limit tobacco use; (b) limit high blood pressure; (c) limit high blood cholesterol; (d) regulate type 2 diabetes; (e) eat a healthier diet; (f) avoid being overweight; and (g) increase level/frequency of physical activity.