DNA methylation signatures of maternal blood leukocytes and placenta predict fetal growth restriction (FGR)

Abstract

The present disclosure provides for a method of treating fetal growth restriction in a fetus comprising obtaining a blood sample from a pregnant subject; isolating cell-free fetal DNA from the blood sample; testing the cell-free fetal DNA for a first DNA methylation level of 0.5800 or less in a group of genes, wherein the group of genes comprises the genes of any one of Tables 19-21, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in differentially methylated regions (DMRs); and when the first DNA methylation level is 0.5800 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of the pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth. Other related methods are disclosed herein.

Description

[0001] DNA METHYLATION SIGNATURES OF MATERNAL BLOOD LEUKOCYTES AND PLACENTA PREDICT FETAL GROWTH RESTRICTION (FGR)

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of priority to, and the benefit of, U.S. Provisional Application No. 63 / 515,280 filed on July 24, 2023, the disclosure of which is hereby expressly incorporated by reference herein in its entirety.

[0004] STATEMENT OF GOVERNMENT SUPPORT

[0005] This invention was made with government support under Grant Nos. 1U01HD087213- 01, awarded by the National Institutes of Health. The government has certain rights in the invention.

[0006] BACKGROUND

[0007] Each year, 3-7% of pregnancies worldwide (more than 25 million) are complicated by fetal growth restriction (FGR), defined by the Society for Maternal-Fetal Medicine as ultrasonography-estimated fetal weight (EFW) and / or abdominal circumference (AC) less than the 10th percentile after 20 weeks' gestation. Fetuses with FGR have a 3 to 5 times higher risk of stillbirth than healthy fetuses. Moreover, those who survive are at elevated risk of cerebral palsy, intracranial hemorrhage, neonatal metabolic acidosis and hypoglycemia, childhood growth abnormalities and neurodevelopmental delays, and adulthood diseases including cardiovascular disease, stroke, and metabolic disorders. Correct diagnosis of FGR, and initiation of antenatal surveillance may reduce stillbirth risk by 40%.

[0008] However, there are four challenges in correctly diagnosing FGR. First, EFW and AC measurements miss 50% of FGR cases. Second, FGR detected by ultrasound sometimes resolves, leading to a normal-weight neonate. But these neonates have many of the same morbidities as those with persistent FGR. Third, 0.7-2.4% healthy fetuses (up to 86,000 / year in the US) are incorrectly diagnosed as having FGR. Fourth, 50% of FGR cases are idiopathic, so there are no risk factors by which to predict their occurrence. Thus, there is a need for a low-cost, accurate test that can be done in the 1stor 2ndtrimesters to predict FGR as defined by ultrasound after 20 weeks' gestation. Such a test would enable physicians to order enhanced surveillance, early delivery if indicated, or low-dose aspirin prophylaxis for those who test positive. Conversely, such a test would allow pregnant women to avoid the anxiety, extra-testing, elevated risk of preterm birth or NICU admission that comes with misdiagnosis.

[0009] The methods disclosed herein address these and other needs.

[0010] SUMMARY

[0011] In accordance with the purposes of the disclosed materials and methods, as embodied and broadly described herein, the disclosed subject matter, in one aspect, relates to fetal growth restriction.

[0012] Thus, in one example, a method of treating fetal growth restriction in a fetus is provided, including obtaining a blood sample from a pregnant subject; isolating cell-free fetal DNA from the blood sample; testing the cell-free fetal DNA for a first DNA methylation level of 0.5800 or less in a group of genes, wherein the group of genes comprises the genes of any one of Tables 19-21, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in differentially methylatedregions (DMRs); and when the first DNA methylation level is 0.5800 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of the pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

[0013] In a further example, a method of treating fetal growth restriction in a fetus is provided, including obtaining a placental villi sample from a placenta via chorionic villus sampling (CVS); isolating fetal DNA from the placental villi sample; testing the fetal DNA for a first DNA methylation level of 0.4400 or less in a group of genes, wherein the group of genes comprises the genes of any one of Table 22, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs); and when the first DNA methylation level is 0.4400 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of a pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth. Additionally, a method of treating fetal growth restriction in a fetus is provided, including obtaining a blood sample from a pregnant subject; isolating cell-free fetal DNA from the blood sample; testing the cell-free fetal DNA for a first DNA methylation level of 0.5800 or less in a group of genes, wherein the group of genes comprises the genes of any one of Tables 23-25, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in differentially methylatedregions (DMRs); and when the first DNA methylation level is 0.5800 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of the pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

[0014] In one examples, a method of treating fetal growth restriction in a fetus is provided, including obtaining a placental villi sample from a placenta via chorionic villus sampling (CVS); isolating fetal DNA from the placental villi sample; testing the fetal DNA for a first DNA methylation level of 0.4400 or less in a group of genes, wherein the group of genes comprises the genes of any one of Table 26, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs); and when the first DNA methylation level is 0.4400 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of a pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

[0015] Additional advantages will be set forth in pail in the description that follows, and in part will be obvious from the description, or may be learned by practice of the aspects described below. The advantages described below will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying figures, which are incorporated in and constitute a part of this specification, illustrate several aspects described below.

[0017] FIGS. 1A-1D show a volcano plot of -loglO (P-value) against delta beta value, displaying differentially methylated CpGs in maternal blood leukocytes or placental villi in GA- and BMI- matched FGR-complicated versus normal (control) pregnancies. DNA samples were compared between maternal blood leukocytes in the first (A), second (B) and third (C) trimester as well as between placental villi following labor (D). CpGs that exhibited a methylation level difference (delta Beta) with p values < 0.01 in FGR vs. control are shown in red dots. Red dots with positive delta Beta value indicate hypermethylated CpGs whereas those with negative delta Beta values indicate hypomethylated CpGs in FGR vs. controls. Dashed red line indicates cut-off for significance.

[0018] FIG. 2 shows a Venn diagram showing differentially methylated CpGs in DNA DMRs of maternal blood leukocytes or placental villi in GA- and BMI-matched FGR-complicated vs. normal (control) pregnancies. In maternal blood leukocytes (MBL), 9 differentially methylated CpGs are commonly detected in the first, second, and third trimester and 16 differentially methylated CpGs are common in the first and second trimester whereas none of 430 differentially methylated CpGs in placental villous (PV) DMRs are common with CpGs detected in maternal blood leukocyte DMRs.

[0019] FIG. 3 shows a Venn diagram displaying differentially methylated CpGs in DNA MVPs in maternal blood leukocytes or placental villi in GA- and BMI-matched FGR-complicated vs. normal (control) pregnancies. 7 differentially methylated CpGs detected in all specimens and 112 differentially methylated CpGs are detected in the first, second, and third trimester in maternal blood leukocytes. MBL; maternal blood leukocytes; PV: Placental villi.

[0020] FIG. 4 shows the Evaluation of DNA methylation. Unmethylated (CpG) and methylated (MCpG) cytosines in MVPs and DMRs. The methylation rate (MR) of each MVP or DMR is calculated by percentage of MCpG.

[0021] FIGS. 5A-5C show a Volcano plot of -loglO (P-value) against differentially methylated CpGs in maternal blood leukocytes. FGR-complicated vs. normal (control) pregnancies matched by GA and maternal BMI exhibit 1999, 3110, and 4046 differentially methylated CpGs in 1st, 2nd, and 3rd trimester maternal blood leukocytes, respectively. Positive and negative delta Beta values indicate hypermethylated and hypomethylated CpGs, respectively, in FGR cases. Dashed red line indicates P < 0.01 in FGR vs. control group.

[0022] FIGS. 6A-6C show KEGG software analysis of genes linked to FGR-specific differentially methylated CpGs. FIG. 6A shows data for the first trimester, FIG. 6B shows data for the second trimester, and FIG. 6C shows data for the third trimester.

[0023] FIG. 7 shows a volcano plot of differentially methylated CpGs in placental villi. We identified 8431 differentially methylated CpG sites in placentas between FGR and control pregnancies. Positive and negative delta Beta values indicate hyper- and hypomethylated CpGs, respectively. Dashed red line indicates P<0.01 in FGR vs. control.

[0024] FIGS. 8A-8B show bioinformatics analysis of genes linked to FGR-specific differentially methylated placental CpGs. A) KEGG analysis links diseases to genes associated with FGR- specific differentially methylated CpGs and B) Gene Ontology analysis links functions of genes associated with FGR-specific differentially methylated CpGs.

[0025] FIG. 9 shows schematic description of research design of Aims 1-3.

[0026] FIGS. 10A-10B show differentially methylated CpGs in 1sttrimester maternal blood leukocytes predict FGR. A) Ratios of hypomethylated to hypermethylated CpGs among 1999 CpGs in FGR-complicated and control pregnancies. B) ROC analysis of these ratios showing 100% sensitivity and 100% specificity (red line) to predict FGR.

[0027] FIG. 11 shows some differentially methylated CpGs shared across 3 trimesters. Venn diagram of differentially methylated CpGs in maternal blood leukocytes from FGR-complicated vs. normal pregnancies.

[0028] FIG. 12 shows some differentially methylated genes related to tobacco use disorder shared across 1st, 2nd, and 3rd trimester maternal blood leukocytes (MBL) and placenta. Venn diagram analysis identified 48 genes (listed at right).

[0029] FIG. 13 shows expression of representative genes associated with FGR-specific DMRs differs in FGR-complicated vs. control placentas. Bars represent mean ± SEM (n= 10 / each).

[0030] FIG. 14 shows a heatmap of differentially methylated CpGs in placental villi. A total of 8431 differentially methylated CpG sites were identified in placentas between FGR-complicated and control pregnancies. Heatmap visualizing the gradual changes in DNA methylation in each CpG sites in FGR-complicated and control placentas. FIGS. 15A-15C show genome wide distribution of differentially methylated CpGs identified in FGR versus normal placental villi. Chromosomal distribution of FGR specific differentially identified CpGs (A) as well as their relation to known CpG islands using University of California, Santa Cruz (UCSC) designation (B), and distribution of sites in relation to known genes according to UCSC Refgene Group association (C). TSS: Transcriptional Start Site, UTR: Untranslated regions, Exon Bound within 20 bp of an exon boundary, / .<?.. the start or end of an exon.

[0031] DETAILED DESCRIPTION

[0032] The following description of the disclosure is provided as an enabling teaching of the disclosure in its best, currently known embodiments. Many modifications and other embodiments disclosed herein will come to mind to one skilled in the art to which the disclosed compositions and methods pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosures are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. The skilled artisan will recognize many variants and adaptations of the aspects described herein. These variants and adaptations arc intended to be included in the teachings of this disclosure and to be encompassed by the claims herein.

[0033] Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

[0034] As can be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure.

[0035] Any recited method can be carried out in the order of events recited or in any other order that is logically possible. That is, unless otherwise expressly stated, it is in no way intended that any method or aspect set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not specifically state in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including matters of logic with respect to arrangement of steps or operational flow, plain meaning derived from grammatical organization or punctuation, or the number or type of aspects described in the specification.

[0036] All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided herein can be different from the actual publication dates, which can require independent confirmation.

[0037] It is also to be understood that the terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosed compositions and methods belong. It can be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0038] Prior to describing the various aspects of the present disclosure, the following definitions are provided and should be used unless otherwise indicated. Additional terms may be defined elsewhere in the present disclosure.

[0039] Definitions

[0040] As used herein, “comprising” is to be interpreted as specifying the presence of the stated features, integers, steps, or components as referred to, but does not preclude the presence or addition of one or more features, integers, steps, or components, or groups thereof. Moreover, each of the terms “by”, “comprising,” “comprises”, “comprised of,” “including,” “includes,” “included,” “involving,” “involves,” “involved,” and “such as” are used in their open, nonlimiting sense and may be used interchangeably. Further, the term “comprising” is intended to include examples and aspects encompassed by the terms “consisting essentially of’ and “consisting of.” Similarly, the term “consisting essentially of’ is intended to include examples encompassed by the term “consisting of.” As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a compound”, “a composition”, or “a disorder”, includes, but is not limited to, two or more such compounds, compositions, or disorders, and the like.

[0041] It should be noted that ratios, concentrations, amounts, and other numerical data can be expressed herein in a range format. It can be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. Ranges can be expressed herein as from “about” one particular value, and / or to “about” another particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it can be understood that the particular value forms a further aspect. For example, if the value “about 10” is disclosed, then “10” is also disclosed.

[0042] When a range is expressed, a further aspect includes from the one particular value and / or to the other particular value. For example, where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure, e.g., the phrase “x to y” includes the range from ‘x’ to ‘y’ as well as the range greater than ‘x’ and less than ‘y’. The range can also be expressed as an upper limit, e.g., ‘about x, y, z, or less’ and should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of ‘less than x’, less than y’, and ‘less than z’. Likewise, the phrase ‘about x, y, z, or greater’ should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of ‘greater than x’ , greater than y ’, and ‘greater than z’ . In addition, the phrase “about ‘x’ to ‘y’”, where ‘x’ and ‘y’ are numerical values, includes “about ‘x’ to about ‘y’”.

[0043] It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or subranges encompassed within that range as if each numerical value and sub-range is explicitly recited. To illustrate, a numerical range of “about 0.1% to 5%” should be interpreted to include not only the explicitly recited values of about 0.1% to about 5%, but also include individual values (e.g., about 1 %, about 2%, about 3%, and about 4%) and the sub-ranges (e.g., about 0.5% to about 1.1%; about 5% to about 2.4%; about 0.5% to about 3.2%, and about 0.5% to about 4.4%, and other possible sub-ranges) within the indicated range.

[0044] As used herein, the terms “about,” “approximate,” “at or about,” and “substantially” mean that the amount or value in question can be the exact value or a value that provides equivalent results or effects as recited in the claims or taught herein. That is, it is understood that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact but may be approximate and / or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art such that equivalent results or effects are obtained. In some circumstances, the value that provides equivalent results or effects cannot be reasonably determined. In such cases, it is generally understood, as used herein, that “about” and “at or about” mean the nominal value indicated ±10% variation unless otherwise indicated or inferred. In general, an amount, size, formulation, parameter or other quantity or characteristic is “about,” “approximate,” or “at or about” whether or not expressly stated to be such. It is understood that where “about,” “approximate,” or “at or about” is used before a quantitative value, the parameter also includes the specific quantitative value itself, unless specifically stated otherwise. As used herein, the term “substantially free,” when used in the context of a composition or component of a composition that is substantially absent, is intended to refer to an amount that is then about 1 % by weight or less, e.g., less than about 0.5 % by weight, less than about 0.1 % by weight, less than about 0.05 % by weight, or less than about 0.01 % by weight of the stated material, based on the total weight of the composition.

[0045] The term “subject” preferably refers to a human in need of treatment with an anti-cancer agent or treatment for any purpose, and more preferably a human in need of such a treatment to treat cancer, or a precancerous condition or lesion. However, the term “subject” can also refer to non-human animals, preferably mammals such as dogs, cats, horses, cows, pigs, sheep and nonhuman primates, among others, that are in need of treatment with an anti-cancer agent or treatment.

[0046] The term “treatment” refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.

[0047] The term “therapeutically effective” refers to the amount of the composition used is of sufficient quantity to ameliorate one or more causes or symptoms of a disease or disorder. Such amelioration only requires a reduction or alteration, not necessarily elimination.

[0048] The term “pharmaceutically acceptable” refers to those compounds, materials, compositions, and / or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problems or complications commensurate with a reasonable benefit / risk ratio.

[0049] Method

[0050] Method of Treating Fetal Growth Restriction in a Fetus

[0051] The present disclosure, in one aspect, provides for a method of treating fetal growth restriction in a fetus comprising: obtaining a blood sample from a pregnant subject; isolating cell- free fetal DNA from the blood sample; testing the cell-free fetal DNA for a first DNA methylation level of 0.5800 or less (e.g., 0.5700 or less, 0.5600 or less, 0.5500 or less, 0.5400 or less, 0.5300 or less, 0.5200 or less, 0.5100 or less, 0.5000 or less, 0.4900 or less, 0.4800 or less, 0.4700 or less, 0.4600 or less, 0.4500 or less, 0.4400 or less, 0.4300 or less, 0.4200 or less; 0.5750 or less, 0.5650 or less, 0.5550 or less, 0.5450 or less, 0.5350 or less, 0.5250 or less, 0.5150 or less, 0.5050 or less, 0.4950 or less, 0.4850 or less, 0.4750 or less, 0.4650 or less, 0.4550 or less, 0.4450 or less, 0.4350 or less, 0.4205 or less) in a group of genes, wherein the group of genes comprises the genes of any one of Tables 19-21, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in differentially methylated regions (DMRs); and when the first DNA methylation level is 0.5800 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of the pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

[0052] In some examples, isolating cell-free fetal DNA from the blood sample comprises using a column or magnetic beads, a phenol chloroform-based method, or a filtration-based method.

[0053] In further examples, the pregnant subject is in a first trimester of pregnancy.

[0054] In certain examples, the group of genes comprises the genes of Table 19.

[0055] In specific examples, the first DNA methylation level is 0.4400 or less (e.g., 0.4395 or less, 0.4390 or less, 0.4385 or less, 0.4380 or less, 0.4375 or less, 0.4370 or less, 0.4365 or less, 0.4360 or less, 0.4355 or less, 0.4350 or less, 0.4345 or less, 0.4340 or less, 0.4335 or less, 0.4330 or less, 0.4325 or less, 0.4320 or less, 0.4315 or less, 0.4310 or less, 0.4305 or less, 0.4300 or less; 0.4359 or less, 0.4358 or less, 0.4357 or less, 0.4356 or less, 0.4355 or less, 0.4354 or less, 0.4353 or less, 0.4352 or less, 0.4351 or less).

[0056] In some examples, the pregnant subject is in a second trimester of pregnancy.

[0057] In further examples, the group of genes comprises the genes of Table 20.

[0058] In certain examples, the first DNA methylation level is 0.5800 or less (e.g., 0.5795 or less, 0.5790 or less, 0.5785 or less, 0.5780 or less, 05775 or less, 0.5770 or less, 0.5765 or less, 0.5760 or less, 0.5755 or less, 0.5750 or less, 0.5745 or less, 0.5740 or less, 0.5735 or less, 0.5730 or less, 0.5725 or less, 0.5720 or less, 0.5715 or less, 0.5710 or less, 0.5705 or less, 0.5700 or less; 0.5769 or less, 0.5768 or less, 0.5767 or less, 0.5766 or less, 0.5765 or less, 0.5764 or less, 0.5763 or less, 0.5762 or less, 0.5761 or less).

[0059] In specific examples, the pregnant subject is in a third trimester of pregnancy.

[0060] In some examples, the group of genes comprises the genes of Table 21.

[0061] In further examples, the first DNA methylation level is 0.5410 or less (e.g., 0.5405 or less, 0.5400 or less, 0.5395 or less, 0.5390 or less, 0.5385 or less, 0.5380 or less, 05375 or less, 0.5370 or less, 0.5365 or less, 0.5360 or less, 0.5355 or less, 0.5350 or less, 0.5345 or less, 0.5340 or less, 0.5335 or less, 0.5330 or less, 0.5325 or less, 0.5320 or less, 0.5315 or less, 0.5310 or less, 0.5305 or less, 0.5300 or less; 0.5405 or less, 0.5404 or less, 0.5403 or less, 0.5402 or less, 0.5401 or less, 0.5399 or less, 0.5398 or less, 0.5397 or less, 0.5396 or less). Further provided herein is a method of treating fetal growth restriction in a fetus comprising: obtaining a placental villi sample from a placenta via chorionic villus sampling (CVS); isolating fetal DNA from the placental villi sample; testing the fetal DNA for a first DNA methylation level of 0.4400 or less (e.g., 0.4380 or less, 0.4360 or less, 0.4340 or less, 0.4320 or less, 0.4300 or less, 0.4280 or less, 0.4260 or less, 0.4240 or less, 0.4220 or less, 0.4200 or less; 0.4390 or less, 0.4370 or less, 0.4350 or less, 0.4330 or less, 0.4310 or less, 0.4290 or less, 0.4270 or less, 0.4250 or less, 0.4230 or less, 0.4210 or less) in a group of genes, wherein the group of genes comprises the genes of any one of Table 23, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs); and when the first DNA methylation level is 0.4400 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of a pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

[0062] In some examples, isolating fetal DNA from the placental villi sample comprises using column or magnetic beads, a phenol chloroform-based method, or a filtration-based method.

[0063] Also provided herein is a method of treating fetal growth restriction in a fetus comprising: obtaining a blood sample from a pregnant subject; isolating cell-free fetal DNA from the blood sample; testing the cell-free fetal DNA for a second DNA methylation level of 0.7100 or less (e.g., 0.7000 or less, 0.6900 or less, 0.6800 or less, 0.6700 or less, 0.6600 or less, 0.6500 or less, 0.6400 or less, 0.6300 or less, 0.6200 or less, 0.6000 or less; 0.7150 or less, 0.7050 or less, 0.6950 or less, 0.6850 or less, 0.6750 or less, 0.6650 or less, 0.6550 or less, 0.6450 or less, 0.6350 or less, 0.6250 or less, 0.6150 or less, 0.6050 or less) in a group of genes, wherein the group of genes comprises the genes of any one of Tables 23-25, wherein the second DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in methylation variable positions (MVPs); and when the second DNA methylation level is 0.7100 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of the pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

[0064] In some examples, isolating cell-free fetal DNA from the blood sample comprises using a column or magnetic beads, a phenol chloroform-based method, or a filtration-based method.

[0065] In further examples, the pregnant subject is in a first trimester of pregnancy.

[0066] In certain examples, the group of genes comprises the genes of Table 23.

[0067] In specific examples, the second DNA methylation level is 0.6900 or less (e.g., 0.6895 or less, 0.6890 or less, 0.6885 or less, 0.6880 or less, 0.6875 or less, 0.6870 or less, 0.6865 or less, 0.6860 or less, 0.6855 or less, 0.6850 or less, 0.6845 or less, 0.6840 or less, 0.6835 or less, 0.6830 or less, 0.6825 or less, 0.6820 or less, 0.6815 or less, 0.6810 or less, 0.6805 or less, 0.6800 or less; 0.6849 or less, 0.6848 or less, 0.6847 or less, 0.6846 or less, 0.6845 or less, 0.6844 or less, 0.6843 or less, 0.6842 or less, 0.6841 or less).

[0068] In some examples, the pregnant subject is in a second trimester of pregnancy.

[0069] In further examples, the group of genes comprises the genes of Table 24.

[0070] In certain examples, the second DNA methylation level is 0.6200 or less (e.g., 0.6195 or less, 0.6190 or less, 0.6185 or less, 0.6180 or less, 0.6175 or less, 0.6170 or less, 0.6165 or less, 0.6160 or less, 0.6155 or less, 0.6150 or less, 0.6145 or less, 0.6140 or less, 0.6135 or less, 0.6130 or less, 0.6125 or less, 0.6120 or less, 0.6115 or less, 0.6110 or less, 0.6105 or less, 0.6100 or less; 0.6199 or less, 0.6198 or less, 0.6197 or less, 0.6196 or less, 0.6195 or less, 0.6194 or less, 0.6193 or less, 0.6192 or less, 0.6191 or less).

[0071] In specific examples, the pregnant subject is in a third trimester of pregnancy.

[0072] In some examples, the group of genes comprises the genes of Table 25.

[0073] In further examples, the second DNA methylation level is 0.7100 or less (e.g., 0.7095 or less, 0.7090 or less, 0.7085 or less, 0.7080 or less, 0.7075 or less, 0.7070 or less, 0.7065 or less, 0.7060 or less, 0.7055 or less, 0.7050 or less, 0.7045 or less, 0.7040 or less, 0.7035 or less, 0.7030 or less, 0.7025 or less, 0.7020 or less, 0.7015 or less, 0.7010 or less, 0.7005 or less, 0.7000 or less; 0.7099 or less, 0.7098 or less, 0.7097 or less, 0.7096 or less, 0.7095 or less, 0.7094 or less, 0.7093 or less, 0.7092 or less, 0.7091 or less).

[0074] Further provided herein is a method of treating fetal growth restriction in a fetus comprising: obtaining a placental villi sample from a placenta via chorionic villus sampling (CVS); isolating fetal DNA from the placental villi sample; testing the fetal DNA for a second DNA methylation level of 0.4800 or less (c.g., 0.4780 or less, 0.4760 or less, 0.4740 or less, 0.4720 or less, 0.4700 or less, 0.4680 or less, 0.4660 or less, 0.4640 or less, 0.4620 or less, 0.4600 or less; 0.4790 or less, 0.4770 or less, 0.4750 or less, 0.4730 or less, 0.4710 or less, 0.4690 or less, 0.4670 or less, 0.4650 or less, 0.4630 or less, 0.4610 or less) in a group of genes, wherein the group of genes comprises the genes of any one of Table 26, wherein the second DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C- phosphate-G-3') (CpGIs) in methylation variable positions (MVPs); and when the second DNA methylation level is 0.4800 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of a pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

[0075] In some examples, isolating fetal DNA from the placental villi sample comprises using a column or magnetic beads, a phenol chloroform-based method, or a filtration-based method.

[0076] Further, the method of treating fetal growth restriction testing the DNA for any of the DNA methylation levels, based on any of the methods of calculating thereof, disclosed herein, and any of the groups of genes as disclosed herein.

[0077] A number of embodiments of the disclosure have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.

[0078] By way of non-limiting illustration, examples of certain embodiments of the present disclosure are given below.

[0079] EXAMPLES

[0080] The following examples are set forth below to illustrate the methods and results according to the disclosed subject matter. These examples are not intended to be inclusive of all aspects of the subject matter disclosed herein, but rather to illustrate representative methods and results. These examples are not intended to exclude equivalents and variations of the present invention, which are apparent to one skilled in the art. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, pails are parts by weight, temperature is in °C or is at ambient temperature, and pressure is at or near atmospheric. There are numerous variations and combinations of reaction conditions, e.g., component concentrations, temperatures, pressures, and other reaction ranges and conditions that can be used to optimize the product purity and yield obtained from the described process. Only reasonable and routine experimentation will be required to optimize such process conditions.

[0081] Example 1: DNA Methylation Signatures of Maternal Blood Leukocytes and Fetal Placenta Predicts Idiopathic Fetal Growth Restriction

[0082] The current findings describe epigenetic signatures of DNA CpG sites which are differentially methylated in the genome of maternal leukocytes and fetal placental villi from women with pregnancies complicated by idiopathic fetal growth restriction (FGR) versus control specimens from pregnant women without FGR. Used herein is the identification of these differentially methylated CpG sites to screen for the likelihood of developing FGR and / or early detection of impending FGR.

[0083] FGR is the second most important cause for perinatal mortality and occurs in about 10% of pregnancies. It is associated with extremes of maternal weight, smoking, gestational hypertension, preeclampsia, type II diabetes, lupus etc. and fetal and placental conditions such as structural and / or genetic anomalies, infections, abruption, circumvallate placenta etc. However, approximately 40 % of FGR is idiopathic. A blood screening test is not available that reliably predicts development of FGR during early pregnancy. Here, by analyzing maternal blood leukocytes in the first, second and / or third trimester as well as placental villi obtained following labor, is the discovery of DNA methylation signatures that predict FGR. These DNA methylation signatures can be used alone for pregnancy screening for early FGR prediction, and / or in combination with ultrasound evaluations to improve clinical diagnosis of FGR.

[0084] CpG designates dinucleotides cytosine and guanine separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') in a single strand of DNA. DNA sites displaying frequent CpG repeats are called CpG islands (CpGIs), which have variable methylation patterns. About 70% of gene promoter regions in mammals contain CpGIs with a strong association between the degree of CpGI methylation and transcriptional activity of these genes. In contrast, 80% of CpG sites not in islands are methylated. Other DNA regions containing CpGIs include: 1 ) intragenic (gene body) locations; 2) CpG shores (located between 2kb upstream and downstream of a promoter region; 3) CpG shelves (located between 2kb upstream and downstream of CpG shores); and 4) open sea (located in DNA regions besides promoters, shores, and shelves).

[0085] Using Infinium MethylationEPIC Kit (Illumina, Inc. San Diego, CA), methylation profiles of CpGs located in gene promoters, bodies, shores, shelves, or open seas were analyzed in DNA samples isolated from maternal blood leukocytes or placental villi obtained from gestational age (GA) and body mass index (BMI) matched pregnant women who subsequently gave birth to neonates with normal birth weight or to neonates with FGR. Leukocytes were obtained from the first (n=15 / group), second (n=15 / group) and third trimester (n=14 / group), while placental villi were obtained from third trimester specimens (n=10 / group) following labor. The quantification of over 850,000 methylation sites of the human genome at single-nucleotide resolution was evaluated by volcano plot (Fig. 1) according to P<0.01 in GA- and BMI-matched FGR-complicated vs. normal (control) pregnancies. Methylation values of DNA CpGIs between FGR vs. control specimens were compared using: (1) Differentially Methylated Regions (DMRs) or (2) Methylation Variable Positions (MVPs). These analyses revealed the following results.

[0086] Analysis of Results According to Differentially Methylated Regions (DMRs):

[0087] - In the DNA of first trimester maternal blood leukocytes, 49 CpGIs are differentially methylated in 10 DMRs, which are linked to 4 genes or located in island, shore, shelf, or open sea (Table 1).

[0088] - In the DNA of second trimester maternal blood leukocytes, 46 CpGIs are differentially methylated in 9 DMRs, which are linked to 4 genes or located in island, shore, shelf, or open sea (Table 2).

[0089] - In the DNA of the third trimester maternal blood leukocytes, 56 CpGIs are differentially methylated in 10 DMRs, which are linked to 5 genes, or located in island, shore, shelf, or open sea (Table 3).

[0090] - In the DNA of in the third trimester placental villi, 430 CpGIs are differentially methylated in 69 DMRs that are linked to 56 genes, 10 miRNAs and 2 InRNAs or located in island, shore, shelf, or open sea (Table 4).

[0091] For specificity and sensitivity analysis, it was first determined average methylation values of all differentially methylated CpGIs (1) or only hypomethylated CpGIs (2) or only hypermethylated CpGIs (3) in identified DMRs for each specimen given in Tables 1-4. Ratio of average values of hypomethylated CpGTs to hypermethylated CpGIs (4) or difference between average values of hypomethylated CpGIs and hypermethylated CpGIs (5) and ratio of difference between average values of hypomethylated CpGIs and hypermethylated CpGIs to average value of all differentially methylated CpGIs (6) were also calculated. By comparing these values in FGR vs. control specimens using Receiver Operating Characteristic Curve (ROC) analysis, the following sensitivities and specificities at the indicated cutoff values for prediction of FGR as given in Table 1.

[0092] 1. Average of all differentially methylated CpGIs in DMRs (A_TOTAL_DMRs).

[0093] 2. Average of hypomethylated CpGIs in DMRs (A_HYPO_DMRs).

[0094] 3. Average of hypermethylated CpGIs in DMRs (A_HYPER_DMRs).

[0095] 4. A_HYPO_DMRs divided by A_HYPER_DMRs.

[0096] 5. Difference between A_HYPO_DMRs and A_HYPER_DMRs.

[0097] 6. Difference between A_HYPO_DMRs and A_HYPER_DMRs divided by A_TOTAL DMRs. Table 1.

[0098] Further analysis of these DMR specific CpGs by Venn diagram (Fig. 2) revealed 9 differentially methylated CpGs in maternal leukocytes common across the 1st, 2nd and 3rd trimester. None of CpGs in DMR is common between maternal leukocytes and placental villi (Fig. 2). This example selected 51 CpGs from 430 placental CpGs: 7 hypermethylated CpGs which display the highest fold increase and 44 hypomethylated CpGs which display the highest fold decrease in FGR vs. control group. This example used these 9 selected CpGs in maternal blood and 51 selected CpGs placental villi to calculate a cutoff value for discrimination of FGR- complicated pregnancies from noimal pregnancies. Therefore, using algorithms and ROC analysis as given above, further assessment of these selected CpGs revealed the following sensitivities and specificities at the indicated cutoff values in the first, second and third trimester specimens (Table 2).

[0099] Table 2.

[0100] ROC analysis was also performed on 25 CpGs by detected in both first and second trimester maternal blood specimens (Fig. 2) as well as on 25 most differentially methylated CpGs from 430 placental CpGs by including 7 highest hypermethylated and 18 lowest hypomethylated CpGs in FGR vs. control group. Evaluation of these selected CpGs by using 6 calculations given above and ROC analysis revealed the following sensitivities and specificities at the indicated cutoff values for prediction of FGR in the first, second and third trimester (Table 3).

[0101] Table 3.

[0102] These findings indicate that all or a part (as exemplified in Tables 2 and 3) of these identified CpGs in the of maternal and fetal genome DMRs can be used alone or in combination to screen for the likelihood of developing FGR and / or early detection of impending FGR in cellular and / or cell free DNA isolated from maternal blood and / or from placental biopsies using different methods including global DNA methylation analysis, pyrosequencing analysis etc. Moreover, protein and / or transcription levels of genes and / or levels of miRNAs and InRNAs provided in Tables 1-4 may also be used to screen for the likelihood of developing FGR and / or early detection of impending FGR in cellular and / or cell free DNA from maternal blood and / or placental biopsies using techniques such as immunoblotting, ELISA, qPCR etc..

[0103] Analysis of results according to Methylation Variable Positions (MVPs):

[0104] - 1999 CpGs are differentially methylated in the 1st trimester maternal blood leukocyte DNA MVPs (Table 5),

[0105] - 3110 CpGs are differentially methylated in the 2nd trimester maternal blood leukocyte DNA MVPs (Table 6),

[0106] - 4046 CpGs are differentially methylated in the 3rd trimester maternal blood leukocyte DNA MVPs (Table 7),

[0107] - 8431CpGs are differentially methylated in the 3rd trimester placental villous DNA MVPs (Table 8).

[0108] For specificity and sensitivity analysis, it was determined that average methylation values of all differentially methylated CpGs (1) or only hypomethylated CpGs (2) or only hypermethylated CpGs (3) in identified MVPs for each specimen given in List 5-8. Ratio of average values of hypomethylated CpGs to hypermethylated CpGs (4) or difference between average values of hypomethylated CpGs and hypermethylated CpGs (5) and ratio of difference between average values of hypomethylated CpGs and hypermethylated CpGs to average value of all differentially methylated CpGs (6) were also calculated. By comparing these values in FGR vs. control specimens using Receiver Operating Characteristic Curve (ROC) analysis, this example determined the following sensitivities and specificities at the indicated cutoff values for prediction of FGR as given in Table 4.

[0109] 1. Average of all differentially methylated CpGc in MVPs (A_TOTAL_MVPs).

[0110] 2. Average of hypomethylated MVPs (A_HYPO_MVPs).

[0111] 3. Average of hypermethylated MVPs (A_HYPER_MVPs).

[0112] 4. A_HYPO_MVPs divided by A_HYPER_MVPs. 5. Difference between A_HYPO_MVPs and A_HYPER_MVPs.

[0113] 6. Difference between A_HYPO_MVPs and A_HYPER_MVPs divided by A_TOTAL MVPs.

[0114] Table 4.

[0115] Select MVPs improve cutoff value between FGR vs. control groups for FGR prediction in either 1st, 2nd or 3rd trimester specimens.

[0116] Further analysis of these MVPs by Venn diagram method (Fig. 3), it was discovered that 7 differentially altered MVPs in all groups, 112 differentially altered MVPs in the first, second and third trimester maternal blood specimens, and 269 differentially altered MVPs in first and second maternal blood specimens. Venn diagram analysis also identified 59 differentially altered MVPs in the first and third trimester maternal blood specimens and 100 differentially altered MVPs in the second and third trimester maternal blood specimens. Moreover, 32, 43 and 56 differentially altered MVPs in placental villi were also detected in the first, second and third trimester maternal blood, respectively (Fig. 3).

[0117] Identification of all differentially methylated CpGs in the genome requires global DNA methylation analysis using next-generation sequencing (NGS) or using array based Infinium MethylationEPIC Kit. Both techniques are expensive and time-consuming. Alternatively, other molecular techniques such as pyro sequencing or array-based analyses can be used to assess methylation status of a specific set of CpGs. Thus, the number of CpGs was reduced using several different approaches as shown below:

[0118] Approach 1: 100 (50 hypermethylated and 50 hypomethylated) CpGs were selected from the tables 5-8, which display the highest fold change in methylation levels in FGR vs. normal specimens. Thus, using same calculations and ROC analysis, further assessed were these select 100 MVPs and determined cutoff values, which have better discrimination between control vs. FGR groups for FGR prediction at 1 .0 sensitivity and 1 .0 specificity in the first, second and third trimester specimens (Table 5).

[0119] Table 5.

[0120] Also obtained were similar results by performing these analyses on top 50 differentially methylated (25 hypermethylated CpGs and 25 hypomethylated CpGs) in MVP lists according to the highest fold change in the first, second and third trimester FGR vs. normal specimens or according to the strongest P values in the first, second and third trimester FGR vs. normal specimens.

[0121] Approach 2: As seen in Venn diagram (Fig. 3), the same 119 CpGs are differentially methylated in the DNA from the first, second and third trimester maternal blood from FGR- complicated pregnancies vs. controls. Thus, using same calculations and ROC analysis, these 119 CpGs (19 hypermethylated GpGs and 100 hypomethylated CpGs) were evaluated and better cutoff values were determined for the prediction of FGR at 1.0 sensitivity and 1.0 specificity in the first, second and third trimester maternal blood (Table 6).

[0122] Table 6.

[0123] Approach 3: According to Venn Diagram (Fig. 3), identified herein:

[0124] 1. 32 differentially methylated CpGs found in both 1 st trimester maternal blood and 3rd trimester placental villi.

[0125] 2. 43 differentially methylated CpGs found in both 2nd trimester maternal blood and 3rd trimester placental villi.

[0126] 3. 65 differentially methylated CpGs found in both 3rd trimester maternal blood and 3rd trimester placental villi.

[0127] Thus, ROC analysis was also performed on these CpGs and detected the following cutoff values with the corresponding sensitivity and specificity for the prediction of FGR in the first (Table 7), second (Table 8) and third trimester (Table 9) specimens. Methylation analysis of these CpGs can be performed using cell free DNA isolated from maternal blood and / or DNA isolated from placental biopsies and / or cell free DNA or biopsies obtained in vitro pre-implantation cultures for the prediction of FGR.

[0128] Table 7.

[0129] Table 8.

[0130] Table 9.

[0131] In conclusion, distinct methylation levels in identified CpGs of the maternal leukocyte DNA and / or fetal placental DNA have highly strong specificity and sensitivity values at the designated cutoffs for the likelihood of FGR development and / or early prediction of impending FGR in the first, second, and / or third trimester. Therefore, in maternal blood leukocytes and / or placental villi and / or cell free DNA specimens, measuring DNA methylation levels by any methods which includes global DNA methylation assay by next-generation sequencing, arraybased DNA methylation assay, pyro sequencing analysis of DNA methylation levels etc. can be used as a screening method for the likelihood of FGR development and / or early prediction of impending FGR.

[0132] Example 2: Expression of Relevant Genes Improving FGR Prediction

[0133] Discussed herein is evidence that DNA methylation patterns could be used to generate such a test. Specifically, maternal blood was collected at 1st, 2nd, and 3rd trimesters from normal (n=15) and FGR-complicated (n=15) pregnancies with no maternal co-morbidities. Methylation was examined at 850,000 CpG (cytosine followed by guanine in 5' to 3' direction of the DNA sequence) sites and identified FGR-specific CpGs (P<0.0T) in methylation variable positions (MVPs) and differentially methylated regions (DMRs) in the 1st, 2nd, and 3rd trimester maternal blood leukocytes. Receiver Operating Characteristic Curve analysis of the ratio of hypermethylated to hypomethylated CpGs predicted FGR with 100% sensitivity and 100% specificity at optimal cut-off values. Similar analyses of post-delivery placentas were performed of the same women and found that MVPs and DMRs also predicted FGR with 100% sensitivity and 100% specificity. This is compelling because placental DNA can be detected in maternal blood. Objective here is to identify the optimal methylation patterns to accurately predict both persistent and resolved idiopathic FGRs in the 1st or 2nd trimester. This was pursued via these three specific aims.

[0134] Aim 1: Identify the optimal maternal leukocyte DNA methylation patterns to predict FGR.

[0135] Determined herein was the sensitivity and specificity of three sets of FGR- specific MVP and DMR patterns using 1stand 2nd trimester maternal leukocyte DNA for early prediction of idiopathic FGR.

[0136] Aim 2: Identify the optimal methylation patterns from cell-free placental DNA to predict FGR.

[0137] Determined herein was the sensitivity and specificity of four sets of MVP and DMR patterns from 1st and 2nd trimester cell-free placental DNA to predict idiopathic FGR. Additionally, determined herein was the sensitivity and specificity of tests that combine cell-free DNA from maternal leukocytes and cell-free placental DNA to predict idiopathic FGR.

[0138] Aim 3: Determine the extent to which including expression of relevant genes improves FGR prediction.

[0139] In a case-control study of patients (n=6 FGR and n=12 healthy) enrolled in the first two years for Aims 1 and 2, measured herein was the expression of mRNA and miRNA linked to differentially methylated regions in 1st and 2udtrimester maternal blood leukocytes and cell-free placental RNA. Measured herein was the abundance of selected secreted proteins. They were monitored in the remainder of the prospectively collected samples from patients in Aims 1 and 2 and it was determined whether including these features improves the sensitivity and specificity of the test.

[0140] Al. The high and inequitable burden of fetal growth restriction: Fetal growth restriction (FGR) is the 2ndleading cause of perinatal mortality and morbidity after preterm birth. Surviving neonates are at elevated risk of cerebral palsy, intracranial hemorrhage, neonatal metabolic acidosis and hypoglycemia, childhood growth abnormalities and neurodevelopmental delays, and adulthood diseases including stroke, cardiovascular disease, and metabolic disorders. Worldwide, FGR complicates 3 to 7% of livebirths, depending on the diagnostic thresholds employed. In 2022, this prevalence meant that 250,000 pregnant people in the US and over 25 million worldwide experienced FGR. The burden of FGR is not equitably distributed, as the incidence is six-fold higher in low / middlc-incomc countries than in high-income countries. Moreover, the incidence of low birth weight, a common downstream consequence of FGR, is higher in Black (14.2%) than in White neonates (6.9%).

[0141] A2. Four challenges in correctly predicting FGR:

[0142] First, the Society for Maternal-Fetal Medicine and the American College of Obstetricians and Gynecologists recommend defining FGR as estimated fetal weight (EFW) and / or abdominal circumference (AC) <10th percentile for the corresponding gestational ages after 20 weeks' gestation. However, even when characteristics such as maternal height, weight, parity, and race / ethnicity and fetal sex are included, EFW and AC measurement are poor predictors of FGR. Thus, even in high-income countries, >50% of FGR cases are undetected.

[0143] Second, FGR can “resolve” before or at delivery, however, neonates from resolved vs. persistent FGR cases had no differences in composite morbidity.

[0144] Third, 1.2% to 2.4% of healthy fetuses are incorrectly diagnosed as having FGR, causing patients to experience unneeded tests, interventions, costs, and anxiety as well as potentially indicated preterm birth and perinatal adverse outcomes.

[0145] Fourth, about 50% of FGR cases are idiopathic. Other cases can be ascribed to factors such as aneuploidy, uniparental disomy, single-gene mutations, extremes of maternal weight, maternal smoking, gestational hypertension, preeclampsia, lupus, infections, abruption, or circumvallate placenta. In such cases, physicians can closely monitor the fetus and prescribe aspirin or early delivery to optimize neonatal health. With no known risk factors for idiopathic FGR cases, nothing to prevent this condition is available.

[0146] A3. Rationale for focusing on DNA methylation: DNA methylation contributes to chromatin organization and gene regulation during prenatal and postnatal life. Environmental factors such as drugs, hormones, and diet can alter DNA methylation, especially during prenatal development. Furthermore, several diseases are associated with disrupted DNA methylation, including Beckwith-Wiedemann syndrome, transient neonatal diabetes mellitus34, FGR, preterm birth, pre-eclampsia, and gestational diabetes. Thus, DNA methylation patterns could reflect disrupted prenatal development and predict FGR.

[0147] A4. Basics of assessing DNA methylation: DNA methyltransferases convert cytosine to 5-methylcytosine in CpG sites (a cytosine and guanine dinucleotide separated by one phosphate in the 5' to 3' direction of a DNA sequence). The human genome contains ~30 million CpGs. Frequent CpG repeats in a DNA region arc called CpG islands. Methylation degree of CpG islands in a gene promoter region is strongly associated with transcriptional upregulation or downregulation, depending on the context of the corresponding gene. Other CpG locations where methylation can affect gene expression include CpG shores, intragenic regions, CpG shelves, and open seas. Global DNA methylation is typically evaluated according to methylation variable position (MVP), which indicates methylation of a single CpG site, or differentially methylated region (DMR), which indicates methylation of several adjacent CpG sites.

[0148] The extent of methylation of an MVP or DMR is calculated as a range between 0% and 100% because samples, such as blood leukocytes, contain different cell types, and methylation profiles differ in each cell type (Fig. 4). Thus, an MVP value indicates the methylation rate of a single CpG site, whereas a DMR value represents the mean methylation rate of adjacent CpGs (Fig. 4).

[0149] A5. DNA methylation in maternal blood lymphocytes can predict FGR: Created herein is the first ever blood screening test to predict FGR during the 1st or 2nd trimesters. This test is inexpensive and requires only a blood draw. Maternal blood was obtained in the 1st, 2nd, and 3rd trimesters from pregnant patients with healthy pregnancies (n=15, including 0 smokers) or pregnancies with FGR (n=15, including 2 smokers). In both groups, the patients had no maternal co-morbidities, and patients were matched for maternal age, gestational age (GA) and body mass index (BMI) at blood collection and parturition. DNA was isolated from maternal blood leukocytes, confirmed the DNA purity by NanoDrop (Thermo Scientific, MA, USA) measurements, and verified the DNA integrity by electrophoresis (insufficient purity of DNA, such as A260 / 280 < 1.8 or > 2.1, protein or RNA contamination were removed by protease and RNAase). DNA was sent to CD Genomics (Shirley, NY), which used an Infinium Methylation- EPIC Kit (Illumina, Inc. San Diego, CA) to measure methylation at single-nucleotide resolution of 85OK CpGs located in gene promoters, bodies, shores, shelves, or open seas. CD Genomics performed bisulfite conversion, hybridization, and bioinformatic services. Normalization of 850,000 CpGs was performed by the Beta-Mixture Quantile procedure, the most widely used method, and the P- value was adjusted by the Benjamini-Hochberg method. There were significant (P < 0.01) methylation differences between FGR-complicated and normal pregnancies in 1999, 3110, and 4046 CpGs in the 1 st, 2nd, and 3rd trimesters, respectively (Figs. 5A-5C).

[0150] To determine whether any of the methylation differences could be used to predict FGR, values and ratios shown in Table 10 were used. Receiver Operating Characteristic Curve (ROC) analysis comparing scores calculated from any of these ratios in FGR-complicated and control group found 100% sensitivity and 100% specificity for early prediction of FGR at the indicated cutoff values in the 1st and 3rd trimesters and 93.7% sensitivity and 100% specificity in the 2nd trimester (Table 10).

[0151] Table 10. FGR cutoff and (Sensitivity and Specificity) for differentially methylated CpGs in maternal blood leukocytes. Methylation values and ratios used throughout table and example: Averaged Values: A - all differentially methylated CpGs; B - hypomethylated CpGs in FGR; C- hypermethylated CpGs in FGR; Ratios: B / c - Ratio ofB to C; (B-C) / A - Ratio of (B minus C) to A.

[0152] Gene ontology (GO) Bioinformatics analysis implicated genes associated with FGR- specific CpGs involved in neuronal, anatomical, and multicellular organism development (date not shown). Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) Bioinformatics analysis calculated the strongest links with genes associated with tobacco use disorder: 371, 564, and 656 genes in the 1st, 2nd, and 3rd trimesters, respectively (Figs. 6A-6C).

[0153] Similarly, methylation of 60, 110, and 130 genes in the 1st, 2nd, and 3rd trimesters, respectively, were linked to chronic alcoholic intoxication (Figs. 6A-6C). These findings are consistent with epidemiologic, clinical, and animal studies reporting smoking and alcohol consumption as leading causes of FGR. For example, in a recent study of 5244 patients, the FGR rate was 10.6% in non-smokers and 22.9% in smokers. Because only 2 of 15 mothers in the FGR group were smokers, women with FGR-complicated pregnancy may have had an epigenetic disposition resembling those in patients with tobacco use disorder.

[0154] A6. Preliminary data suggesting that methylation of cell-free placental DNA can predict FGR: Post-labor placentas were obtained from GA- and BMI-matched women (the same patients as in A5) without maternal comorbidities who delivered healthy neonates or neonates with FGR (n=10 / cach). This example isolated DNA from pieces of placental villi approximately one inch from the umbilical cord area. As detailed in A5, this example used the Infinium Methylation- EPIC Kit (Illumina, Inc.) to quantify methylation status of 850,000 CpG sites. This analysis revealed 8431 differentially methylated CpG sites in FGR vs. control groups (P<0.0h Fig. 7). Of these, only 32, 43, and 65 differentially methylated sites were also present in the 1st, 2nd, and 3rd trimester maternal blood leukocytes, respectively. Using the same approach as in Box 1, ROC analysis of these 8431 placental CpGs revealed 100% sensitivity and 100% specificity at the indicated cutoff value for 3rd trimester FGR prediction (Table 11).

[0155] Table 11. FGR cutoff and (sensitivity and specificity) values for 8431 placental CpGs.

[0156] Functional analysis of genes associated with these 8431 CpGs by KEGG bioinformatics linked 992 and 178 genes to tobacco use disorder and alcoholic intoxication, respectively (Fig. 8 A), as observed in maternal blood leukocytes (Figs. 6A-6C). GO bioinformatics analysis also revealed that several cellular and biological processes associated with nervous, anatomical, and organism development were affected in FGR specimens (Fig. 8B).

[0157] B. Innovation

[0158] The differential methylation patterns tested here will likely provide high specificity and sensitivity to predict idiopathic FGR. This test changes care by allowing physicians to provide proper follow-up and timely delivery of FGR cases or target low-dose aspirin prophylaxis. Such approaches help to reduce FGR-associated stillbirth, perinatal morbidity and mortality, and diseases occurring in childhood and adulthood.

[0159] C. Approach

[0160] Overview of research design: the goal is to determine the optimal set(s) of differentially methylated CpG patterns discovered in the case-control study for early prediction of FGR (Fig. 9).

[0161] Aim 1 focused on FGR-specific differentially methylated MVPs and DMRs in maternal blood leukocytes. 358 patients were enrolled (allowing for 10% loss to follow-up and of whom ~14 are anticipated to have idiopathic FGR) and collect maternal blood at the 1st and 2nd trimesters. The sensitivity and specificity of three sets of 1 st and 2nd trimester maternal leukocyte methylation patterns were tested to predict idiopathic FGR as defined by ultrasound detected EFW or AC <10th percentile after 20 weeks' gestation.

[0162] Aim 2 focused on differentially methylated CpG patterns in cell-free placental DNA in maternal serum. Samples were from the same patients as in Aim 1, collect cell-free DNA, and determine the sensitivity and specificity of four sets of methylation patterns to predict idiopathic FGR. Additionally, the sensitivity and specificity of tests that combine cell-free DNA from maternal leukocytes and cell-free placental DNA were determined to predict idiopathic FGR.

[0163] Aim 3 focused on expression of relevant genes to potentially improve FGR prediction. Expression of mRNAs, miRNAs, and proteins linked to FGRspecific differentially methylated regions in 1st and 2nd trimester maternal blood leukocytes and cell-free placental DNA were measured. They were monitored in the prospectively collected samples from patients in Aims 1 and 2 and determined herein was whether including these features improves the sensitivity and specificity of the test.

[0164] Cl. Aim 1: Identify the optimal maternal leukocyte DNA methylation patterns to predict FGR.

[0165] CIA. To identify the optimal set of differentially methylated CpG sites in maternal blood leukocytes for an accurate test, three analyses were performed, yielding three sets of methylation patterns:

[0166] First, in the case-control study, this example identified 1999 CpG sites that were differentially methylated in maternal blood leukocytes between patients with FGR and those with healthy pregnancies (Fig. 5A). The ratio of hypomethylated to hypermethylated CpG sites significantly differed between the two groups (Fig. 10A). Moreover, ROC analysis showed that this ratio provided 100% sensitivity and 100% specificity to predict idiopathic FGR at 12 weeks' gestation (Fig. 10B). This example found similar results when this example analyzed differentially methylated CpG sites from maternal blood leukocytes collected in the 2nd (3110 sites) and 3rd (4046 sites) trimesters (Fig. 5B, 5C and Table 10). Thus, the first set of methylation patterns will be the 1999 differentially methylated CpGs in the 1st trimester and the 3110 differentially methylated CpGs in the 2nd trimester.

[0167] Second, this example compared the FGR-specific differentially methylated CpGs from the 1st, 2nd, and 3rd trimesters by Venn diagram. As seen in Fig. 11, 119 CpGs were differentially methylated (100 hypomethylated and 19 hypermethylated) in maternal blood leukocytes from all three trimesters. This example calculated ratios as in Box 1, and ROC analysis revealed that these ratios provided 100% sensitivity and 100% specificity to predict FGR (Table 12). Thus, the second set of methylation patterns will be these 119 common CpGs.

[0168] Table 12. FGR cutoff and (sensitivity and specificity) for selected differentially methylated CpGs in maternal blood leukocytes.

[0169] Third, this example performed DMR-based analysis. With this approach, this example identified 19 DMRs containing 105 differentially methylated CpG sites between FGR complicated vs. control group in the 1st, 2nd, and 3rd trimesters. Table 13 shows the genes associated with these DMRs. Then, this example calculated ratios as in Box 1. ROC analysis revealed that these ratios provided 100% sensitivity and 100% specificity to predict FGR in the 1st, 2nd, and 3rd trimesters (Table 12). Thus, the third set of methylation patterns will be these 105 CpGs.

[0170] Table 13. Details of 19 FGR specific DMRs containing 105 differentially methylated CpG sites and associated genes.

[0171] C1B. Patient recruitment: this example proposes a prospective cohort study of pregnant women attending Morsani College of Medicine of University of South Florida and Washington University School of Medicine in St. Louis centers for prenatal care, this example will recruit 358 women (allowing for 10% loss to follow-up) at their ultrasound appointment at 12-14 weeks' gestation. Potential participants will be included if they have singleton pregnancies and agree to blood draws at 12-14 weeks and 20-22 weeks and ultrasound at 20-22 and 32-36 weeks. Potential participants will be excluded if they have fetal anomalies, twins, or higher order gestations. Pregnancy will be dated using the earliest ultrasound parameter (crown-rump length) obtained as part of the participant's clinical care or at enrollment.

[0172] CIC. FGR diagnosis: Fetal ultrasound biometry including head circumference, femur length, and abdominal circumference will be obtained at the 2nd and 3rd trimester visits and used to estimate fetal weight. FGR will be defined as EFW or AC <10th percentile at >20 weeks' gestation.

[0173] CID. Sample collection and processing: During the 1st and 2nd trimester visits, maternal blood will be collected into four tubes (10 ml / each):

[0174] Tube 1 will be an EDTA tube (BD Inc., Franklin Lakes, NJ) to be used for DNA isolation from maternal blood leukocytes, which will be isolated and prepared as previously described53, for analyses in Aim 1.

[0175] Tube 2 will be a PAXgene Blood RNA tube (Qiagen, Hilden, Germany), which will immediately stabilize total RNA in the blood samples. RNA will be isolated within 3 hours of the collection using a PAXgene Blood RNA Kit (Qiagen) and stored at - 80°C for analyses in Aim 3.

[0176] Tube 3 will be a Vacutainer tube (BD Inc.) used for cell free DNA / RNA isolation for analyses in Aim 2 (cell-free DNA) and Aim 3 (cell-free RNA). After collection, the whole blood will undergo standard processing to obtain serum, which will be mixed with Buffer RPL to lyse exosomes and release RNA as instructed in QIAamp- ccfDNA / RNA Kit (Qiagen). The sample will then be mixed with Buffer RPP and centrifuged to precipitate proteins. Isopropanol will be added to the supernatant, which will then be applied to a midi-spin column. The column will be washed twice with different buffers provided in the kit before the cell-free DNA and RNA are eluted.

[0177] Tube 4 will be a Vacutainer tube (BD Inc.). Blood will undergo standard processing to obtain serum, which will be transferred into a clean cold polypropylene, apportioned into 0.5 ml aliquots, and stored at -80°C. These samples will be used for multiplex ELIS As in Aim 3. CIE. Methylation analyses: To analyze DNA methylation in maternal blood leukocytes from the 1st and 2nd trimesters (Tube 1), this example will use targeted methyl (targeted bisulfite) sequencing, a multiplex-targeted CpG methylation analysis method based on next-generation sequencing technology. Thus, this example will include customized hybridization probes only for the 1999 and 3110 CpGs from the 1st and 2nd trimesters, respectively (Figs. 5A-5C and Table 10) plus the 105 common CpGs from DMR based analysis in the 1st, 2nd, and 3rd trimesters (Table 13). Thus, a total of 4821 CpGs [(1999+3110+105) - (119+274 CpGs, which are detected in both 1st and 2nd trimester, see Venn diagram in Fig. 11)] will be included for targeted methyl sequencing (See letter of support from CD Genomics).

[0178] Isolated genomic DNA samples will be sent to CD-genomics for bisulfite conversion, DNA amplification, and hybridization with the custom customized probes, quantification and normalization of signals, and bioinformatics services. Briefly, genomic DNA (500 ng) will be treated with sodium bisulfite with an EZ-96 DNA Methylation Kit (Zymo Research, Irvine, CA)55 to convert unmethylated cytosine into uracil. Methylated cytosines will remain unchanged. Multiplex PCR using an optimized primer combination will be followed by PCR amplification and library construction and sequencing using a 2 x 150 bp terminal pairing pattern on double-ends by an Illumina MiSeq (Illumina, CA, USA) platform. Each CpG’s methylation rate will be represented as the percentage of methylated cytosines in total cytosines. Both fully methylated and unmethylated DNA regions will be used as positive and negative controls. This example will use 10 DNA samples (5 control and 5 FGR).

[0179] CIF. Data analyses: After obtaining methylation values of all 4821 CpGs, this example will sort out these CpGs into the three groups described in CIA: First, 1999 1st trimester- specific CpGs or 3110 2nd trimester- specific CpGs (Figs. 5A-5C). Second, 119 CpGs common in all trimesters (Fig. 11 and Table 3). Third, DMR-specific 105 CpGs (Tables 12-13). For each group, this example will calculate ratios as in Box 1. Cutoff values identified in the case-control study to predict cases as idiopathic FGR or non-FGR (Table 14).

[0180] Table 14. Cutoff values for idiopathic FGR in maternal blood leukocytes.

[0181] CIG. Anticipated outcomes, potential challenges, and alternative approaches: this example anticipates that at least one of the methylation patterns will predict FGR cases with >93% sensitivity and >93% specificity at the cutoff values determined in the case control studies (Table 14). This example anticipates identifying tests that arc accurate with maternal blood leukocytes from both the 1st and 2nd trimesters. One concern regards inter-assay reliability across two sites. However, during the case-control study, this example performed feasibility studies and identified target CpGs in repeated experiments in the same participants at 1st, 2nd, and 3rd trimesters on two different benches. Inter-assay variability was only 1-2% differences in methylation values of the same specimens between the two benches. This example also saw high consistency among all trimesters (intra-assay reproducibility). Thus, this example do not expect any difficulties with these analyses. By evaluating only 4821 CpGs, this example may find higher standard deviation than in the case-control study, which may lower sensitivity / specificity. If this is the case, this example will use the full 850,000 CpG sites methylation kits as this example did in the case-control study.

[0182] C2. Aim 2: Identify the optimal methylation patterns from cell-free placental DNA to predict FGR.

[0183] C2A. Background, rationale, and preliminary data regarding differential methylation patterns to test: In the case-control study, this example identified CpGs that were differentially methylated between placentas of patients with FGR and placentas of patients with healthy pregnancies (see A6). Here, this example plan to monitor these CpGs in cell free placental DNA in maternal serum. To begin to identify the optimal set of differentially methylated CpG sites in placental DNA for an accurate test, this example performed four analyses, yielding four sets of methylation patterns:

[0184] First, in the case-control study, analysis of placental specimens from FGR-complicated vs. control pregnancies revealed 8431 differentially methylated MVPs (Fig. 7). This example calculated ratios as in Box 1, and ROC analysis of these 8431 CpGs detected 100% sensitivity and 100% specificity to predict idiopathic FGR at the indicated cutoff values (Table 15). Thus, the first set of methylation patterns will be these 8431 CpGs in cell-free placental DNA from maternal serum in the 1st and 2nd trimesters.

[0185] Second, out of these 8431 differentially methylated placental CpGs, this example extracted the top 100 differentially methylated (50 hypermethylated and 50 hypomethylated) CpGs in FGR-complicated vs. normal group, this example then calculated ratios as in Box 1. ROC analysis of these ratios revealed 100% sensitivity and 100% specificity at the indicated cutoff values for FGR prediction (Table 15). Thus, the second set of methylation patterns will be these 100 CpGs in cell-free placental DNA in the 1st and 2nd trimester maternal scrum.

[0186] Third, the case-control study revealed 69 DMRs containing 430 CpGs linked to 56 genes, 10 miRNAs, and 3 long non-coding RNAs (IncRNAs). The top 20 DMRs are listed in Table 16. Calculation of ratios from these 430 CpGs as in Box 1 and subsequent ROC analysis of these ratios revealed 91% sensitivity and 91% specificity for FGR prediction at the indicated cutoff values (Table 15), Thus, the third set of methylation patterns will be these 430 CpGs in cell-free placental DNA from maternal serum in the 1st and 2nd trimesters.

[0187] Fourth, analysis using the top 50 differentially methylated CpGs of these 430 CpGs revealed 100% sensitivity and 100% specificity for FGR prediction at the indicated cutoff values (Table 15). Thus, the fourth set of methylation patterns will be these 50 CpGs in cell-free placental DNA from maternal serum in the 1st and 2nd trimesters.

[0188] Table 15. Cutoff values of circulating cell-free placental DNA for idiopathic FGR.

[0189] Table 16. Top 20 DMRs in FGR complicated vs. normal (control) placentas. C2B. Methylation analyses: As noted in C2A, this example identified 8861 FGR-specific differentially methylated placental CpGs (8431 CpGs from MVP based analysis and 430 CpGs from DMR based analysis) over 850,000 CpG sites. However, this analysis used 500 ng of placental DNA, and this example are unlikely to isolate this much cell-free placental DNA from maternal serum. Thus, this example will use targeted methyl (targeted bisulfite) sequencing, which requires 10-25 ng DNA, and hybridization probes to capture regions of interest but with deeper sequencing depth and higher coverage than whole genome bisulfite sequencing as described in several studies. Thus, this example customized hybridization probes targeting 8861 FGR-specific CpGs (see letter of support of CD Genomics).

[0190] Circulating cell-free DNA (25 ng) from 1st and 2nd trimester maternal blood (n=358 / trimester, see C5 for sample size calculation) collected into Tube 3 (Vacutainer tube, BD Inc.) will be isolated as described in CID. DNA samples will be sent to CD Genomics (see letter of support) for bisulfite conversion, DNA amplification and hybridization using target specific probes, sequencing and quantification, normalization, and bioinformatic services as described in C1E. Fully methylated and unmethylated DNA regions will serve as positive and negative controls. This example will use 5 normal and 5 FGR complicated placentas from the case-control study in optimizations of panel (customized hybridization probes) and as experimental controls and reference values.

[0191] C2C. Data analyses: After obtaining methylation values of all 8861 CpGs, this example will sort out these CpGs into the four groups described in C2A: First, 8431 MVP-specific CpGs (Table 15). Second, the top 100 differentially methylated CpGs of these 8431 (Table 15). Third, DMR specific 430 CpGs (Table 15). Fourth, the top 50 differentially methylated CpGs of 430 CpGs of placental DMRs (Table 15). This example will use the maternal serum percentages of placental cell-free DNA and maternal cell-free DNA, which were deconvoluted in previous studies. Methylation values of these CpGs in maternal blood leukocytes from the case-control study will be used as reference values, which are similar in both control and FGR complicated pregnancies (except for 32, 43, and 65 CpGs in the 1st, 2nd, and 3rd trimester maternal blood leukocytes, as described in A6). Thus, methylation values of cell-free placental CpGs in the 1st trimester maternal serum will be determined by the following algorithm:

[0192] Circulating cell-free DNA results obtained from targeted methyl sequencing will represent total methylation value of that maternal and placental CpG (TMV-MP-CpG). In the 1 st trimester maternal serum, 85% of cell-free DNA consists of 80% cell-free DNA from maternal blood leukocytes (CFD-MBL= 85%) and 5% cell-free DNA from fetoplacental cells (CFD-P = 5%).

[0193] The mean methylation value of these 8861 CpGs in maternal blood leukocytes from the case-control study will be reference methylation values for each maternal blood leukocyte CpG (RMV-MBL-CpG).

[0194] P-CFD CpG value - [(85% * TMV-MP-CpG) - (80% * RMV-MLB-CpG)] / 5%

[0195] Then, this example will calculate ratios as in Box 1 for each CpG set. The cutoff values will be those found in the case control study (Table 15). In that study, simulation of this formula confirmed only 1-2% alterations of methylation values of placental CpGs. In the 2nd trimester, due to known changes in percentage of maternal and placental cell free DNA as reported in prior deconvolution studies, 59, 60 this example will use CFD-MBL as 75% and CFD-P as 7%.

[0196] C2D. Rationale and feasibility of combining CpGs from cell-free DNA from maternal blood leukocytes and placental DNA: A test that includes a limited set of both maternal and placental CpGs could be ideal, as it would reflect both the mother and fetus and might require fewer CpGs to test and thus be less expensive than other tests. Such a test is feasible to develop because it is possible to detect both maternal and placental cell free DNA in maternal serum. In support, previous studies59,60 examined 5,820 tissue-specific DNA methylation markers in cell- free DNA from plasma of pregnant patients. They found that -80%, -75%, and -65% of total cell free DNA in the 1st, 2nd, and 3rd trimesters, respectively, originated from maternal blood leukocytes. They also found that -5%, -7%, and -19% of total cell-free DNA in the 1st, 2nd, and 3rd trimesters, respectively, originated from the placenta. The remaining cell-free DNA at each trimester originated from other maternal tissues such as the liver, pancreas, and kidney. Thus, FGR-specific differentially methylated maternal and placental CpGs can be evaluated together in cell-free DNA samples in maternal serum.

[0197] One challenge in such combined methylation profiling is that maternal and placental methylation of the same CpGs may mask differential methylation found in FGR-complicated pregnancies. To eliminate this potential masking, this example will focus on placental- specific and maternal- specific differentially methylated CpGs as follows:

[0198] C2D-1. Select placental-specific CpGs for cell-free DNA analysis: In the case-control study, this example identified maternal blood leukocyte CpGs that had <5% methylation and did not significantly differ in the 1 st, 2nd, and 3rd trimester between FGR-complicated v.y. control pregnancies. Because these maternal CpGs have low methylation values, they should have little masking effect on the methylation differences of FGR-specific placental CpGs. Thus, this example matched these maternal CpGs with their cognates in placental CpGs and selected those placental CpGs exhibiting >20% methylation value in control group (n=10) and displaying at least 10% higher or lower methylation difference in the FGR group (n=10). With these restrictions, this example identified 281 placental (226 hypomethylated and 54 hypermethylated) CpGs. This example calculated ratios of these 281 CpGs as in Box 1 in A5 and performed ROC analysis, revealing 100% sensitivity and 100% specificity to predict FGR (Table 17). Thus, these 281 CpGs will be used as placental- specific CpGs in predicting idiopathic FGR.

[0199] Table 17. Cutoff values and (sensitivity and specificity) for 281 FGR-specific placental CpGs.

[0200] C2D-2. Select maternal-specific CpGs for cell-free DNA analysis: this example selected 119 maternal CpGs identified from MVP based analysis (Table 12) and 105 maternal CpGs from DMR based analysis (Table 12) for the following 3 reasons:

[0201] 1) Methylation values of these 224 CpGs showed consistently significant differences between FGR vs. control in the 1st, 2nd, and 3rd trimester maternal blood leukocytes.

[0202] 2) The B / C ratio and (B-C) / A ratio of these CpGs exhibited 100% sensitivity and 100% specificity for FGR prediction in the 1st, 2nd, and 3rd trimester (Table 12).

[0203] 3) Placental cognates of these 224 CpGs were not differentially methylated in FGR vs. control pregnancies in the case-control study.

[0204] C2E. Methylation analyses: this example will isolate 25 ng of cell-free DNA from maternal serum in the 1st and 2nd trimesters and process it for targeted methyl sequencing as described. For this analysis, this example will use customized hybridization probes consisting of 505 (281 placental and 224 maternal) differentially methylated CpGs (See letter of support from CD Genomics). Circulating cell-free DNA from 1st and 2nd trimester maternal blood (n=358 / trimester, see sample size calculation) collected into Tube 3 (Vacutainer tube, BD Inc.) will be isolated as described in CID. Isolated cell-free genomic DNA samples from maternal serum will be sent to CD Genomics for bisulfite conversion, DNA amplification and hybridization using target specific probes, sequencing, quantification, and bioinformatic services. Both fully methylated and unmethylated DNA regions will he used as positive and negative controls. This example will use 10 placental DNA samples (5 control and 5 FGR) from the previous case-control study in optimizations of panel (customized hybridization probes) and as experimental controls reference CpG controls. Then, as described in C2D1 and 2, this example will separately analyze the 281 placental CpGs and 224 maternal CpGs. This example will calculate their values by using the algorithm in C2C and separately calculate ratios as in Box 1. The cutoff values will be from the case-control study (Table 14 for maternal CpGs and Table 17 for placental CpGs).

[0205] C2F. Anticipated outcomes, potential challenges, and alternative approaches: this example anticipates identifying placental- specific CpG methylation patterns (C2A-C2C) and combination maternal and placental cell-free DNA methylation patterns (C2D-C2E) that predict idiopathic FGR cases with over 95% sensitivity and 95% specificity in the 1st and / or 2nd trimester. Because isolation of 500 ng circulating cell-free DNA would require too much maternal blood, this example instead propose to perform targeted methyl sequencing. This method requires 10-25 ng cell free DNA and produces compatible results with global methylation analysis and is now widely preferred for biomarker / diagnostic studies of cell-free DNA. this example do not anticipate any technical issues with targeted methyl sequencing. However, this example could instead perform pyro sequencing analysis, which has high single nucleotide resolution.

[0206] C3. Aim 3: Determine the extent to which including expression of relevant genes improves FGR prediction.

[0207] C3A. DNA methylation is an epigenetic mechanism to regulate gene expression, and proper DNA methylation is used for cell differentiation and embryonic and fetal development. As gene and protein expression are more direct readouts of gene activity than DNA methylation, including them may yield a more user friendly and cost-effective test that can be done routinely in clinical labs to predict FGR than methylation patterns alone or may improve the prediction when combined with methylation patterns. Thus, this example will examine expression of mRNAs and proteins linked to FGR-specific DMRs in maternal blood leukocytes (C3B) and cell- free placental RNA in maternal blood (C3C).

[0208] C3B. Measure expression of genes associated with FGR-specific DMRs in maternal blood leukocytes: C3B-1. Genes to study: this example will examine expression of 58 genes, chosen as follows: First, as listed in Table 13, this example identified FGR-spccific maternal DMRs linked to the following 10 genes: GALNT18, CRY2, ADGRD1, EGFR, PPP2R3A, KIRREL3, LINC02458, GNAS, Lnc-F13Al-2 and ERICH I . Second, KEGG analysis revealed a link between tobacco use disorder and 371, 564, and 656 genes associated FGR-specific differentially methylated CpGs in maternal blood leukocytes from the 1st, 2nd, and 3rd trimesters, respectively (Figs. 6A-6C). KEGG analysis also identified 992 genes associated with tobacco use disorder in the term placental villi (Figs. 8A-8B). Among these tobacco use disorder-linked genes, Venn- diagram analysis revealed 48 genes present in maternal blood at each trimester and placental villi (Fig. 12).

[0209] C3B-2. Gene expression analysis methods'. In the case-control study, this example did not collect blood in a manner suitable for RNA preservation. Here, maternal blood leukocytes collected into Tube 2 (the PAXgene Blood RNA tube, Qiagen, see CID) within 3 hours of the collection will be processed for total RNA isolation with a PAXgene

[0210] Blood RNA Kit (Qiagen) as described. After DNase treatment, reverse transcription will be performed with the SuperScript II kit (Invitrogen, Carlsbad, CA) in two steps: template RNA and random primers will be incubated at 65 °C for 5 min to eliminate any secondary structures, and then the buffer, dNTP mix, and enzyme will be added. The reaction will be carried out at 42°C for 1 h. this example will use the QuantiGene Plex assay (Invitrogen, Waltham, MA), which is a fast and high-throughput method to simultaneously quantify up to 80 genes of interest in a single well of a 96-well plate. The QuantiGene Plex assay will be performed for 58 genes, which include 10 genes (listed in Table 13) and 48 tobacco use disorder related genes (listed in Fig. 12) using Luminex XMAP MAGPIX instrument (EMD Millipore, Burlington, MA).

[0211] C3B-3. Samples to analyze: this example will first retrospectively perform this analysis in 1st and 2nd trimester maternal blood leukocytes from FGR-complicated (n= 6) vs. control (n=12) specimens at the end of year two. If this example determine that any of these genes alone or together predict FGR with >90% sensitivity and specificity, then this example will prospectively analyze the remaining 200 specimens starting in year three. Each sample will be run in duplicate, and the average will be used for the 2-AACt (cycle threshold) method to calculate relative levels after normalized to 5 housekeeping gene levels as internal controls. Results will be reported as a fold change. C3C. Measure expression levels of cell-free mRNA, miRNA, and secretory proteins of placental DMRlinkcd genes in maternal scrum:

[0212] C3C-1. Genes to study: this example will examine expression of 104 genes, 10 miRNAs, and 2 IncRNAs, chosen as follows: First, the case-control analysis uncovered 56 genes, 10 miRNAs and 3 IncRNAs linked to 69 FGR-specific placental DMRs (Table 9). This example selected 5 of these genes - ADPRHL1, WRB, TWIST 1, DLG5, and PYCARD - and compared the abundance of their transcripts in FGR-complicated (n= 10) and GA- and BMImatched normal placentas (n=10) by qPCR. This example normalized results to ACTB.

[0213] This example found that expressions of TWIST1, WRB, and ADPRHL1 were significantly higher in FGR-complicated than in control placentas, whereas expressions of DLG5 and PYCARD were significantly lower in FGR-complicated than in control placentas (Fig. 13). Second, this example will examine the 48 genes shown in Fig. 12 to be associated with tobacco use disorder.

[0214] C3C-2. Methods for analyzing gene expression: Circulating cell-free RNA / miRNA / lncRNA will be isolated from Tube 3 as described in CID, and this example will use the QuantiGene Plex assay (Invitrogen)72,73 to assess 104 genes, 10 miRNAs and 2 IncRNAs using Luminex XMAP MAGPIX instrument (EMD Millipore). All samples will be run in duplicate, and the average used for each sample. The 2-AACt (cycle threshold) method will be used to calculate relative expression levels after normalized to 5 housekeeping gene levels as internal controls (See Auth / Key Bio / Chem). Results will be reported as a fold change.

[0215] C3C-3. Proteins to study and methods: Among the 56 genes in Table 18, 6 (C1QTNF1, TSPEAR, GPHA2, OLFML3, TSPEAR, WFDC1) are translated to secretory proteins that can be assessed by ELISA. Additionally, among the 48 genes in Fig. 12, 5 (HHIPL2, LTBP1, NCAM1, NRG1, OLFAIi) are translated to secretory proteins. Thus, this example will measure these 11 proteins in maternal serum by multiplex ELISA, 74, 75 using samples from Tube 4, processed as described in CID. Table 18. 56 genes, 10 miRNAs and 3 IncRNAs linked to 69 FGR-specific placental DMRs.

[0216] C3C-4. Samples to analyze', this example will initially complete this analysis retrospectively on 1 st trimester and 2nd trimester maternal sera from FGR-complicated (n=6) vs. control (n=12) specimens at the end of year two. If these genes or proteins alone or together exhibit a strong FGR prediction value (>90% sensitivity and specificity), the remaining specimens stalling from year three will be prospectively analyzed.

[0217] C3D. Anticipated outcomes, potential challenges, and alternative approaches: Results from C3B will reveal whether transcription of genes linked to FGR-specific DMRs are up- or down-regulated in FGR-complicated vs. control pregnancies. Results from C3C will reveal whether abundance of proteins linked to FGR-specific placental DMRs in maternal serum predict FGR. For both gene and protein expression, this example will perform ROC analysis for expression of affected genes or proteins individually or in combination or by their ratios to determine predictive value for FGR. This example have ample experience with qPCR, so this example do not anticipate any challenges with using the well-established QuantiGene Plex (multiplex qPCR) assay. Nevertheless, this example will confirm upregulation and down- regulated genes by routine qPCR, and if this example face challenges with QuantiGene Plex, this example will switch to qPCR analysis.

[0218] C4. Data management and analysis for all three aims:

[0219] Primary outcomes: Methylation pattern-based prediction of development of idiopathic FGR is the primary outcome of this study. FGR will be defined as EFW or AC <10th percentile for gestational age at >20 weeks' gestation using an estimated fetal weight chart. A secondary outcome will be the association of methylation patterns and FGR with other maternal pathologies such as pre-eclampsia or hypertensive disorders of pregnancy. Preeclampsia will be diagnosed according to the criteria of the ACOG80,81: blood pressures of >140 / 90 mmHg on two readings 4 hours apart with onset after 20 weeks' gestation, and proteinuria (>0.3 gm / 24 hours, protcin / crcatininc ratio of at least 0.3 mg / dL, or >1+ urine dipstick).

[0220] Maternal demographic data to be collected from the institutional computerized ultrasound databases, Epic databases, and by interviews of patients by nurse coordinators:

[0221] Age, Date of Birth, Race, Gravida and Parity,

[0222] Previous medical history.

[0223] Previous obstetrical history: Year for each Delivery / Termination, Gestational Age at Delivery, Mode of Delivery, Sex, Weight, and Health status of the Newborn.

[0224] Risk factors for FGR: Smoking, Illicit Drug Use, Pre-gestational Diabetes, Chronic Hypertension, Pregnancy Induced Hypertension.

[0225] Present pregnancy information: Last menstrual period and expected date of delivery.

[0226] Maternal height, weight, and blood pressure (mean arterial blood pressure) at the prenatal visit that coordinates with each study visit.

[0227] Ultrasound infomation'. Crown Rump Length, Gestational Age by last menstrual period, Gestational Age by Ultrasound, fetal biometry (biparietal diameter, femur length, AC and EFW in the second and third trimesters).

[0228] Antepartum records'. Any hospital admissions and relevant diagnoses and therapies

[0229] Delivery-related information'. Date of Delivery, Gestational Age at Delivery, Labor Onset, Mode of Delivery, Indication for Delivery.

[0230] Neonatal outcomes'. Birth Weight, Apgar score, Cord Blood Gas Analysis, Admission to NICU, Number of Days in NICU, Need for Intubation, Diagnosis / Complications (especially of respiratory distress syndrome, Neonatal Seizures, Hypoxic-Ischemic Encephalopathy, Necrotizing Enterocolitis, Intraventricular Hemorrhage, Periventricular Leukomalacia), Age at Discharge, Neonatal Well-being at Discharge.

[0231] C5. Power and statistical analysis: The primary focus of the proposal is to use maternal and fetal DNA methylation scores to develop a highly sensitive clinical test predicting idiopathic FGR. The prevalence of FGR is 7% and about 50% of cases are idiopathic, corresponding to ~4% idiopathic FGR prevalence. Thus, this will be a prospective cross-sectional analytic study. A total sample size of 325 (N=358 with the potential 10% dropout rate, which includes at least 14 patients with idiopathic FGR) achieves 97% power to detect a sensitivity of 98% using a two- sided binomial test and 100% power to detect a specificity of 98% using a two-sided binomial test. The sensitivity and specificity under the null hypothesis are assumed to be 63%. The target significance level is 0.05. The actual significance level achieved by the sensitivity test is 0.04 and achieved by the specificity test is 0.05. The prevalence / pre-test probability of idiopathic FGR is 4%. Rho Spearman's coefficients will be calculated to establish correlations between methylation values and various numeric variables (mRNA and miRNA levels, or FGR and normal, EFW and / or AC values).

[0232] Statistical analysis will be performed with SigmaStat version 3.0 software (Systat Software, San Jose, CA). Kolmogorov-Smirnov test will assess data for normality. Pair-wise multiple comparisons by One-Way ANOVA will be followed by the post-hoc Tukey test for parametric distribution or Student-Newman-Keuls test for nonparametrical distribution. Two groups comparison will use a / -test or a Mann-Whitney U test for parametric or non-parametric distribution, respectively.

[0233] From the previous NICHD UO1 award, a case-control study discovered a highly sensitive and specific DNA methylation signature of FGR in the maternal and placental genomes. This proposal aims to develop the first ever blood screening test, predicting idiopathic FGR. Such a test would dramatically change care by allowing physicians to provide proper follow-up and timely delivery of FGR cases or target low-dose aspirin prophylaxis, thereby reducing FGR- associated stillbirth, perinatal mortality, and morbidity. Moreover, it will lower the number of healthy newborns with FGR misdiagnosis, thereby reducing anxiety and extra testing in expectant mothers and likelihood of preterm delivery and NICU admission of healthy neonates misdiagnosed with FGR. This example are in the process of converting the provisional patent to a complete patent. Thus, in future studies this example plan to: 1) commercialize this test in partnership with potential companies, via an STTR grant application to expand its use across the US and worldwide; 2) assess if FGR-specific DNA methylation signature(s) detected in women across the pregnancy can also be detected at pre- and / or post-pregnancy stages; 3) perform functional studies delineating molecular mechanisms of genes linked to FGR-specific CpGs, regulation of epigenetic memory by environmental factors including tobacco, alcohol and / or maternal-fetal epigenetic memory inheritance. Example 3: Identification of Differentially Methylated Regions of Placental DNA CpG Islands Predicts Fetal Growth Restriction

[0234] Introduction

[0235] Epigenetic changes such as DNA methylation affect gene expression as one of the key mechanisms by which environmental exposures and genetic variations influence placental functions, i.e. nutrient and gas transport, immunologic and hormonal regulations, which primarily regulate fetal growth. Fetal growth restriction (FGR), a major cause of perinatal mortality and morbidity, occurs in about 10% of pregnancies and is associated with maternal weight, smoking, gestational hypertension, preeclampsia, autoimmune disorders, and genetic abnormalities. However, about 40% of FGR is idiopathic. FGR is defined as estimated fetal weight (EFW) and / or abdominal circumference (AC) <10th percentile for the corresponding gestational ages after 20 weeks' gestation. However, EFW and AC are poor predictors of FGR even when maternal height, weight, parity, and race and fetal sex are included. Identification of differentially methylated regions (DMR) in FGR complicated placental DNA detected genomic pathway(s) contributing to aberrant fetal growth and can be used as predictive biomarkers of FGR.

[0236] Material and Methods

[0237] Genome-wide DNA methylation comparison was performed in gestational age, maternal age and BMI-matched control (n=10) versus idiopathic FGR-complicated (n=10) placentas using Infinium Human MethylationEPIC kit with a coverage of over 850,000 CpG sites (Illumina, San Diego, CA). DMRs were determined atP<0.07. The values of DMR were compared to predicting FGR using Receiver Operating Characteristic Curve analysis. The results were validated by qPCR and compared by a t-test with P<0.05 considered statistically significant.

[0238] Results

[0239] FGR complicated placentas exhibit altered global DNA methylation profiles. Identified herein were 8431 differentially methylated CpG sites including 7662 hypomethylated and 769 hypermethylated CpGs, revealing a global hypomethylation trend in FGR complicated vs. control placentas (FIG. 7 and FIG. 14). Chromosome 1 and 2 displayed higher numbers of differentially methylated CpGs while chromosome 16 showed the lowest numbers of differentially methylated CpGs (FIG. 15A). Analysis of methylation location in the genome revealed that 17% of these CpGs are located in N- and S-shores which are up to 2 kb from a CpG island and 8% in N- and S-shclvcs which arc from 2 to 4 kb from a CpG island, whereas 67% arc in Open Sea that refers to isolated regions which do not have a specific designation (FIG. 15B). The majority of these CpGs were found in gene body, and about 23% of them located in TSS1500, TSS200 or 5’UTR (FIG. 15C).

[0240] KEGG software analysis of genes linked to FGR-specific differentially methylated CpGs. Functional analysis of genes associated with these 8431 CpGs by KEGG bioinformatics linked 992 and 178 genes to tobacco use disorder and alcoholic intoxication, respectively (FIG. 8A). KEGG analysis also revealed that several cellular and biological processes associated with nervous, anatomical, and organism development were affected in FGR specimens (FIG. 8B).

[0241] Identification of DMR in specific genes linked to FGR pathogenesis. Defined herein are DMRs showing a significant difference of methylation levels between FGR and control placental samples. These analyses revealed FGR-specific 430 placental CpGs clustered in 69 DMRs linked to 56 protein-, 10 miRNA- and 2 InRNA coding genes. Among them, top 20 DMRs are shown in Table 16. The majority of DMR had lower DNA methylation (Hypo-DMR), while only a small portion showed increased methylation levels (Hyper-DMR) in FGR complicated versus control placental specimens.

[0242] Receiver Operating Characteristic Curve (ROC) analysis comparing scores calculated from both of these ratios in FGR-complicated and control specimens revealed 100% sensitivity and 100% specificity for prediction of FGR at the indicated cutoff values (Table 15). Among hypomethylated DMRs, selected herein was ADPRHL1, WRB, TW1ST1, DLG5, and PYCARD, and compared the abundance of their transcripts in FGR-complicated (n=10) and GA- and BMI- matched normal placentas (n=10) by qPCR. This analysis revealed that TWIST1, WRB, and ADPRHL1 levels were significantly higher, whereas DLG5 and PYCARD levels were significantly lower in FGR complicated vs. control placentas (FIG. 13).

[0243] Conclusion

[0244] The results identified idiopathic FGR specific placental DNA methylation changes, suggesting that placental epigenetic mechanisms impair fetal growth. The results also support that cell-free fetal DNA methylation profile can be used as early biomarkers of impending FGR. Other advantages which are obvious, and which are inherent to the invention, will be evident to one skilled in the art. It will be understood that certain features and sub-combinations are of utility and may be employed without reference to other features and sub-combinations. This is contemplated by and is within the scope of the claims. Since many possible embodiments may be made of the invention without departing from the scope thereof, it is to be understood that all matter herein set forth or shown in the accompanying drawings is to be interpreted as illustrative and not in a limiting sense.

[0245] Table 19. 49 differentially methylated CpGIs in 10 DMRs, which are linked to 4 genes or located in island, shore, shelf, or open sea in the first trimester maternal blood leukocytes.

[0246] Table 20. 46 differentially methylated CpGIs in 9 DMRs, which are linked to 4 genes or located in island, shore, shelf, or open sea in the second trimester maternal blood leukocytes.

[0247] Table 21. 56 differentially methylated CpGIs in 10 DMRs, which are linked to 5 genes or located in island, shore, shelf, or open sea in the third trimester maternal blood leukocytes.

[0248] Table 22. 430 differentially methylated CpGIs in 69 DMRs, which are linked to 56 genes, 10 miRNAs and 2 InRNAs or located in island, shore, shelf, or open sea in the third trimester placental samples.

[0249] Table 24. Differentially methylated CpGs in 2ndtrimester maternal blood leukocytes from FGR I’v. controls.

[0250] Table 25. Differentially methylated CpGs in 3rd trimester maternal blood leukocytes from FGRs vs. controls. Table 26. Differentially methylated CpGs in FGR 1’5. control placental specimens. References

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Claims

CLAIMSWhat is claimed is:

1. A method of treating fetal growth restriction in a fetus comprising: obtaining a blood sample from a pregnant subject; isolating cell-free fetal DNA from the blood sample; testing the cell-free fetal DNA for a first DNA methylation level of 0.5800 or less in a group of genes, wherein the group of genes comprises the genes of any one of Tables 19- 21, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in differentially methylated regions (DMRs); and when the first DNA methylation level is 0.5800 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of the pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

2. The method of claim 1, wherein isolating cell-free fetal DNA from the blood sample comprises using a column or magnetic beads, a phenol chloroform-based method, or a filtration- based method.

3. The method of any one of claims 1-2, wherein the pregnant subject is in a first trimester of pregnancy.

4. The method of claim 3, wherein the group of genes comprises the genes of Table 19.

5. The method of any one of claims 3-4, wherein the first DNA methylation level is 0.4400 or less.

6. The method of any one of claims 1-2, wherein the pregnant subject is in a second trimester of pregnancy.

7. The method of claim 6, wherein the group of genes comprises the genes of Table 20.

8. The method of any one of claims 6-7, wherein the first DNA methylation level is 0.5800 or less.

9. The method of any one of claims 1-2, wherein the pregnant subject is in a third trimester of pregnancy.

10. The method of claim 9, wherein the group of genes comprises the genes of Table 21.

11. The method of any one of claims 9-10, wherein the first DNA methylation level is 0.5410 or less.

12. A method of treating fetal growth restriction in a fetus comprising: obtaining a placental villi sample from a placenta via chorionic villus sampling (CVS); isolating fetal DNA from the placental villi sample; testing the fetal DNA for a first DNA methylation level of 0.4400 or less in a group of genes, wherein the group of genes comprises the genes of any one of Table 22, wherein the first DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs); and when the first DNA methylation level is 0.4400 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of a pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

13. The method of claim 12, wherein isolating fetal DNA from the placental villi sample comprises using column or magnetic beads, a phenol chloroform-based method, or a filtration-based method.

14. A method of treating fetal growth restriction in a fetus comprising: obtaining a blood sample from a pregnant subject; isolating cell-free fetal DNA from the blood sample; testing the cell-free fetal DNA for a second DNA methylation level of 0.7100 or lessin a group of genes, wherein the group of genes comprises the genes of any one of Tables 23- 25, wherein the second DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in methylation variable positions (MVPs); and when the second DNA methylation level is 0.7100 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of the pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

15. The method of claim 14, wherein isolating cell-free fetal DNA from the blood sample comprises using a column or magnetic beads, a phenol chloroform-based method, or a filtration-based method.

16. The method of any one of claims 14-15, wherein the pregnant subject is in a first trimester of pregnancy.

17. The method of claim 16, wherein the group of genes comprises the genes of Table 23.

18. The method of any one of claims 16-17, wherein the second DNA methylation level is 0.6900 or less.

19. The method of any one of claims 14-15, wherein the pregnant subject is in a second trimester of pregnancy.

20. The method of claim 19, wherein the group of genes comprises the genes of Table 24.

21. The method of any one of claims 19-20, wherein the second DNA methylation level is 0.6200 or less.

22. The method of any one of claims 14-15, wherein the pregnant subject is in a third trimester of pregnancy.

23. The method of claim 22, wherein the group of genes comprises the genes of Table 25.

24. The method of any one of claims 22-23, wherein the second DNA methylation levelis 0.7100 or less.

25. A method of treating fetal growth restriction in a fetus comprising: obtaining a placental villi sample from a placenta via chorionic villus sampling (CVS); isolating fetal DNA from the placental villi sample; testing the fetal DNA for a second DNA methylation level of 0.4800 or less in a group of genes, wherein the group of genes comprises the genes of any one of Table 26, wherein the second DNA methylation level is calculated via an average of all differentially methylated islands of cytosine and guanine dinucleotides separated by only one phosphate in the 5' to 3' direction (5'-C-phosphate-G-3') (CpGIs) in methylation variable positions (MVPs); and when the second DNA methylation level is 0.4800 or less, selecting a treatment from the following: monitoring the fetus during pregnancy of a pregnant subject, tracking movements of the fetus, admitting the pregnant subject to a hospital prior to going into labor, inducing early delivery of the fetus, performing an emergency Cesarean section on the pregnant subject, administering corticosteroids to the pregnant subject, or administering corticosteroids to the fetus upon birth.

26. The method of claim 25, wherein isolating fetal DNA from the placental villi sample comprises using a column or magnetic beads, a phenol chloroform-based method, or a filtration-based method.

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