Methods and device useful in the prediction of preterm birth and hypertensive disorders of pregnancy

Abstract

Disclosed herein are methods and kits for assessing the risk of preterm birth (PTB) and hypertensive disorders of pregnancy (HDPs) by analyzing immune responsiveness in a subject.

Description

METHODS AND DEVICE USEFUL IN THE PREDICTION OF PRETERM BIRTH AND HYPERTENSIVE DISORDERS OF PREGNANCYCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This PCT application claims priority to, and the benefit of. U.S. Provisional Patent Application No. 63 / 610,784, filed December 15, 2023, which is incorporated by reference herein in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with Government Support under Grant No. F31NR014605, R01HD110503, and K23NR017902 awarded by the National Institutes of Health. The Government has certain rights in the invention.FIELD

[0003] The present disclosure relates methods of quantifying risk for hypertensive disorders of pregnancy (HDP) and preterm birth (PTB) in a pregnant subject.BACKGROUND OF THE INVENTION

[0004] A healthy intrauterine environment coupled with sufficient time in the womb are basic yet critical prerequisites to the health of every newborn. Yet, adverse birth outcomes affect numerous families. For example, preterm birth (i.e., birth before 37 weeks gestation) affects 15 million global births each year, including more than 10% of births in the United States (US). In fact, preterm birth (PTB) rates have increased in recent decades and a striking racial disparity has persisted, with Black Americans 1.5 times more likely to give birth preterm than White Americans. This is particularly problematic considering that PTB is the leading cause of death during the first month of life and conveys heightened risk for death across childhood. And, among survivors, healthcare is lengthier, more intensive, and more costly, particularly for the earliest births. Thus, the national and global burden of PTB is substantial and of increasing concern.

[0005] More than 60% of PTBs are spontaneous (sPTBs), brought about by premature uterine contractions or rupture of membranes. The most common gross pathologic finding in sPTB isinflammation. In fact, during term and preterm labor, maternal immune cells are known to rush to reproductive tissues, produce large amounts of cytokines, and induce a pro-inflammatory cascade that feeds forward until birth of the baby. In other words, labor appears to be an acute inflammatory event. Yet, during pregnancy, maternal immune markers in serum and cervical fluid fail to clinically predict who will versus will not progress to future sPTB. The mechanisms driving initiation of the pro-inflammatory cascade of labor remain elusive. Moreover, hypertensive disorders of pregnancy (HDP) underlie 17% of preterm births (PTB). With HDPs serving as a leading cause of severe maternal morbidity and PTB serving as the leading cause of severe neonatal morbidity, these commonly comorbid conditions are among our most salient public health problems. Like sPTB, immune perturbations are commonly noted in the context of HDP. Despite this, the biology driving risk for HDP is poorly understood, biologically informed screening tools quantifying risk for HDP are lacking, and therapies capable of mitigating risk for HDP have not been identified.

[0006] Interestingly, quiescent pregnancy is now recognized as an immunotolerant state, which involves a shift toward mucosal immunity and away from cellular innate immunity. In fact, maternal leukocytes show a predictable pattern of adaptation in cellular innate immunity across pregnancy, responding less vigorously to innate immune challenge in early pregnancy and more and more vigorously as pregnancy progresses, particularly during labor. This pattern of adaptation can protect against early pregnancy fetal allograft rejection. It’s also possible that cellular innate immunity gradually reapproximates a responsive phenotype in preparation for the inflammatory events of labor and premature re-approximation of this phenotype is a keyrisk factor for sPTB. Deviations from this expected pattern of reapproximating innate immunity may also underlie complications, namely HDPs.

[0007] Indeed, dysregulated immune responsiveness has been linked to PTB and HDPs. Inappropriate immune responsiveness can be appreciated based on the pattern of inflammatory- mediator production following the stimulation of pattern recognition receptors (PRRs) such as toll-like receptors (TLRs). PRRs initiate an innate immune response upon stimulation with pathogen- or damage-associated molecular patterns. For example, TLR4, a cell surface PRR, is among the most consistently implicated PRRs in sPTB. While TLR4 is best known for its role in recognizing gram-negative bacterial products such as outer membrane components of E. coli. the receptor also initiates an innate immune response upon binding to endogenous ligands such as heat shock proteins and viral components such as the SARS-CoV-2 spikeprotein. E. coli species are among the most commonly isolated pathogens in sPTB and higher levels of circulating heat shock proteins have been noted prior to sPTB. Risk for sPTB also appears to be about 1.5X higher during SARS-CoV-2 infection.

[0008] What is needed in the art is the application of the measurement of toll-like receptor (TLR)-mediated immune responsiveness witnessed during pregnancy to predict risk for PTB and HDPs.SUMMARY OF THE INVENTION

[0009] Disclosed herein are methods of determining the likelihood of PTB and / or HDPs in a subject, the method comprising obtaining a sample from a subject, manipulating the sample, assaying immune markers in the manipulated sample to determine status of TLR- mediated immune responsiveness in the subject, and inputting values reflective of TLR- mediated immune responsiveness into an algorithm, thereby determining the likelihood of PTB and / or HDPs.

[0010] Also disclosed are methods of determining the likelihood of PTB and / or HDPs in a subject, the method comprising obtaining samples from a subject, determining deoxyribonucleic acid (DNA) methylation (DNAm) and messenger ribonucleic acid (mRNA) expression status of at least one marker reflecting the regulation of TLR-mediated immune responsiveness in the samples, and inputting DNAm and / or mRNA expression values reflective of the regulation of TLR-mediated immune responsiveness into an algorithm, thereby determining the likelihood of PTB and / or HDPs.

[0011] Also disclosed herein are kits comprising materials necessary7for clinically determining TLR-mediated immune responsiveness in a subject, wherein the markers are immune markers produced following TLR stimulation.BRIEF DESCRIPTION OF DRAWINGS

[0012] FIG. 1 shows TLR-mediated signaling pathways.

[0013] FIGS. 2A, 2B and 2C show TLR-mediated immune responsiveness at 30 + 2 weeks gestation in the prediction of future birth timing in 92 generally healthy pregnant Black Americans. Associations among lipopolysaccharide (LPS) stimulated TNF-a, IL-8, and TNFRII production and birth timing were best modelled using cubic, quadratic, and quadratic functions, respectively. Together, these markers explained 18.9% (adj. R2 = 0.122) of the variance in future birth timing (TNF-aA3: beta = 2. 152. t (84) = 2.36, p = 0.021; IL-8A2: beta = 1.052, t (84) = 3.06, p = 0.003; TNFRIIA2: beta = 0.316, t (84) = 2.45, p = 0.016). Resultsare presented for the transformed [i.e., A / (-(days gestation) + 288 + 1), with 288 serving as the maximum recorded value and one serving as a constant] and untransformed outcome variable (i.e., days gestation at birth). Overall, individuals with low and high TNF-a production, IL-8 production in the mid-range, and low and high TNFRII production had the earliest birth rates.

[0014] FIGS. 3A, 3B and 3C show TLR-mediated immune responsiveness in the prediction of future sPTB in 81 individuals at risk for sPTB due to the presence of one or more risk factors. Adjusting for white blood cell count and applying polynomial parameterization, the following immune markers were evaluated following LPS stimulation of maternal peripheral whole blood: interferon (IFN)-y, interleukin (IL)-12. IL-6, IL- 1 p, tumor necrosis factor (TNF)-a, IL- 8, TNF receptor I (TNFRI), TNFRII, and IL- 10. After applying the methods of Hosmer, Lemeshow, and Sturdivant for model reduction, future sPTB was predicted with an accuracy of 97.5%, AUC of 0.93, sensitivity of 81.8%, and specificity of 100.0%. The positive predictive value (PPV) was 100.0%, negative predictive value was 97.2%, likelihood ratio positive (LR+) was 81.8, and likelihood ratio negative (LR-) was 0.18. FIG. 3 A shows sensitivity factored against the false positive rate for the prediction of sPTB using our novel method for risk prediction. FIG. 3B shows the results of the net benefit analysis for the determination of applied probability threshold. FIG. 3C shows the classification of sPTB according to TLR-mediated immune responsiveness and the true outcome of sPTB versus no sPTB in the assessed cohort.

[0015] FIGS. 4A, 4B and 4C show prediction of future sPTB per clinical history. This set of analyses was completed as a comparator to the assessment of immune responsiveness and modeled future sPTB based on history of PTB, history' of sPTB, and history of more than one sPTB. AUCs were 0.67, 0.69, and 0.58, respectively. Consistent with the extant literature, lack of PTB and lack of sPTB in prior pregnancy only provided value in ruling out potential for sPTB (PPVs of 0.0%, NPVs of 87.1%). More than one prior sPTB was required to provide information toward ruling in potential sPTB cases (PPV=50.0%, NPV=88.9%). FIG. 4A shows the classification of risk for sPTB according to the history of PTB. FIG. 4B shows the classification of risk for sPTB according to history of sPTB. FIG. 4C shows the classification of risk for sPTB according to history' of >1 sPTB.

[0016] FIGS. 5 A, 5B and 5C show TLR-mediated immune responsiveness in the prediction of HDPs among a heterogeneous cohort. Again, we examined the ability of LPS-stimulated whole blood cytokine production capacity, controlling for white blood cell and platelet counts, to discriminate between future HDP-affected versus normotensive pregnancy. Stimulated releaseof immune markers was examined upon challenge. The reduced model predicted future HDP with an AUC of 0.78 (sensitivity=29.4%, specificity=98.4%, PPV=83.3%, NPV=84.0%). The steps were repeated for isolating cases of gestational hypertension vs. preeclampsia and eclampsia. The reduced model predicted gestational hypertension with an AUC of 0.84 (sensitivity =45.5%, specificity=95.8%. PPV=62.5%, NPV=92.0%). Our reduced model predicted preeclampsia / eclampsiawith an AUC of 0.90 (sensitivity=50.0%, specificity=92.0%, PPV=33.3%, NPV=95.8%). FIG. 5A show's the classification of risk for any HDP according to our novel algorithm. FIG. 5B shows the classification of risk for gestational hypertension according to our novel algorithm. FIG. 5C shows the classification of risk for preeclampsia or eclampsia according to our novel algorithm.

[0017] FIG. 6 shows the cells of the innate and adaptive immune system present in a peripheral whole blood sample, the starting source for our method of quantifying TLR-mediated immune responsiveness. Expected patterns of TLR expression by cell type are also presented.

[0018] FIG. 7 shows an overview of a method used to measure immune responsiveness, starting with samples collected in the clinical setting.

[0019] FIGS. 8A, 8B, 8C, 8D and 8E show an analysis of patterns of early third trimester maternal peripheral leukocyte DNA methylation and future spontaneous birth timing. First, epigenome-wide DNA methylation was compared among future sPTB cases (n=5) versus variable-ratio matched (on age, education, and nulliparity) sFTB controls (n=l l) using the DESeq, Minfi, and BCurve methods. We identified a total of 12,862 differentially methylated cytosines (DMCs; 598 by DESeq, 3,120 by Minfi, and 9,672 by BCurve). FIG. 8A shows that there was some overlap in results by analytical method, with 38 (0.3%) DMCs overlapping the DEseq, Minfi, and BCurve outputs. Of these 38 DMCs, 27 (71.1%) co-located with known genes, 11 (28.9%) resided on CpG islands, 7 (18.4%) on CpG shores, 4 (10.5%) on CpG shelves, and 16 (42. 1%) in open sea (FIG. 8B). FIG. 8C shows that based on results from casecontrol comparisons and Ingenuity Pathway Analysis, 20 priority amplicons w ere designed and 16 were successfully amplified for second method validation among the full analytical sample with spontaneously initiated birth (n=50). Among all participants with spontaneously initiated birth (n=50), we applied lasso with the penalty parameter selected by 10-fold cross-validation to select amplicons for inclusion in the final model for the prediction of spontaneous birth timing. Two nonzero coefficients were chosen: 1) average methylation of the amplicon flanking cg09667261 (chromosome 4 in proximity to ELF2) and 2) average methylation of theamplicon flanking cg25412831 (chromosome 11 in proximity to BDNF). Associations among average methylation of the two selected amplicons and future spontaneous birth timing were then examined, with higher levels of average amplicon methylation in proximity' to ELF2 (t = -2. 13, 0 = -0.280, p = 0.039) and lower levels of average amplicon methylation in proximity to BDNF (t = 2.82, = 0.372, p = 0.007) during the early third trimester of pregnancy associated with significantly earlier spontaneous birth timing (FIGS. 8D and 8E). Findings remained similar after adjusting for maternal age, marital status, education, pre-pregnancy BMI, smoking during pregnancy, nulliparity', gestational age at sampling, and time of sampling (ELF2: t = - 2.09, p = -0.335, p = 0.043; BDNF: t = 2.31, p = 0.353, p = 0.027).DETAILED DESCRIPTION OF THE INVENTIONDefinitions

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary' skill in the art to which this disclosure belongs.

[0021] Ranges can be expressed herein as from “about” one particular value, and / or to “about” another particular value. By “about” is meant within 10% of the value, e.g., within 9, 8, 7, 6, 5, 4, 3, 2, or 1% of the value. When such a range is expressed, another aspect includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another aspect. It will 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.

[0022] The term “comprising” and variations thereof as used herein is used synonymously with the term “including” and variations thereof and are open, non-limiting terms. Although the terms “comprising” and “including” have been used herein to describe various embodiments, the terms “consisting essentially of’ and “consisting of’ can be used in place of “comprising” and “including” to provide for more specific embodiments and are also disclosed. Throughout the description and claims of this specification the word “comprise” and other forms of theword, such as “comprising” and “comprises,” means including but not limited to, and is not intended to exclude, for example, other additives, components, integers, or steps.

[0023] As used in the specification and claims, the singular form “a,” “an”, and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an agent” includes a plurality of agents, including mixtures thereof.

[0024] As used herein, the terms "may," "optionally," and "may optionally" are used interchangeably and are meant to include cases in which the condition occurs as well as cases in which the condition does not occur. Thus, for example, the statement that a formulation "may include an excipient" is meant to include cases in which the formulation includes an excipient as well as cases in which the formulation does not include an excipient.

[0025] "Inhibit," "inhibiting," and "inhibition" mean to decrease an activity, response, condition, disease, or other biological parameter. This can include but is not limited to the complete ablation of the activity, response, condition, or disease. This may also include, for example, a 1 % reduction in the activity, response, condition, or disease as compared to the native or control level. Thus, the reduction can be a 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%, or any amount of reduction in between as compared to native or control levels. Specifically, in the context of this invention, “inhibit” can mean that undesired pregnancy or post-partum outcomes are inhibited.

[0026] By “reduce,” or “abrogate,” (used interchangeably) or other forms of the word, such as “reducing” or “reduction,” or “abrogating” or “abrogation” is meant lowering of an event or characteristic. It is understood that this is typically in relation to some standard or expected value, in other words it is relative, but that it is not always necessary for the standard or relative value to be referred to.

[0027] By “increase” or other forms of the word, such as “increasing,” is meant raising or elevating. It is understood that this is ty pically in relation to some standard or expected value, in other words it is relative, but that it is not always necessary for the standard or relative value to be referred to.

[0028] “Control” refers to a sample or standard used for comparison with an experimental sample. In some embodiments, the control is a sample obtained from a healthy subject (or a plurality of healthy subjects), such as a subject or subjects not expected or known to have a particular polymorphism. In additional embodiments, the control is a historical control or standard reference value or range of values (such as a previously tested control sample orplurality of such samples), or group of samples that represent baseline or normal values. A positive control can be an established standard that is indicative of a given state. In some embodiments a control genomic feature (such as a gene or area of methylation) can possess or lack a certain characteristic, and is used in assays for comparison with a test nucleic acid, to determine if the test nucleic acid is different.

[0029] “Detecting” is used herein to identify the existence, presence, or fact of something. General methods of detecting are known to the skilled artisan and may be supplemented with the protocols and reagents disclosed herein. For example, included herein are methods of detecting an inflammatory marker or methylation state. This can occur either on the level of detecting the nucleic acid, or patterns of methylation of the nucleic acid, or detecting the level or presence of a protein.

[0030] An “isolated” biological component (such as a nucleic acid molecule or protein) has been substantially separated, produced apart from, or purified away from other biological components. Nucleic acid molecules as well as proteins which have been “isolated” include nucleic acids molecules and proteins purified by standard purification methods, as well as those chemically synthesized. Isolation does not require absolute purify and can include nucleic acid molecules as well as proteins that are at least 50% isolated, such as at least 75%, 80%, 90%, 95%. 98%. 99% or even 100% isolated.

[0031] A “nucleic acid” is a deoxy ribonucleotide or ribonucleotide polymer, which can include analogues of natural nucleotides that hybridize to nucleic acid molecules in a manner similar to naturally occurring nucleotides. In a particular example, a nucleic acid molecule is a single stranded (ss) DNA or RNA molecule, such as a probe or primer. In another particular example, a nucleic acid molecule is a double stranded (ds) nucleic acid, such as a target nucleic acid. Examples of modified nucleic acids are those with altered backbones, such as peptide nucleic acids (PNA).

[0032] “Probes,” as used herein, refer to short nucleic acid molecules, usually DNA or RNA oligonucleotides, typically of about 3-150 nucleotides in length, and more specifically, about 6-100 nucleotides in length, used to detect the presence of a complementary target nucleic acid strand in a sample. All or a portion of a probe can be annealed to a complementary target nucleic acid strand by nucleic acid hybridization to form a hybrid between the probe and the target DNA strand.

[0033] Typically, the nucleic acid portion of the probe includes at least about 6 contiguous nucleotides, such as at least about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 or about 50 contiguous nucleotides, that are complementary to a target nucleic acid molecule, such as 20-70 nucleotides, 20-60 nucleotides. 20-50 nucleotides. 20-40 nucleotides, or 20-30 nucleotides. Probes can also be of a maximum length, for example no more than 20, 25, 25, 40, 50, 75 or 100 nucleotides in length. The specificity of a particular probe typically increases with an increase in the number of complementary nucleotides on the probe.

[0034] The probe can also include additional nucleotides that are not complementary to the target nucleic acid molecule. The additional nucleotides can be used, for example, for detection of the probe in a sample. In several embodiments, the probes disclosed herein include a hybridization portion that is complementary to a test nucleic acid sequence, and a nucleation portion (that can associate with metal nanoclusters) or an enhancer portion (that can enhance the fluorescence of metal nanoclusters associated with the nucleation portion). The additional nucleotides can be located 5' or 3' of the hybridization nucleotides.

[0035] Methods for preparing and using nucleic acid probes are described, for example, in Sambrook et al. (In Molecular Cloning: A Laboratory Manual, CSHL, New York, 1989), Ausubel et al. (ed.) (In Current Protocols in Molecular Biology, John Wiley & Sons. New York, 1998), and Innis et al. (PCR Protocols, A Guide to Methods and Applications, Academic Press, Inc., San Diego, Calif, 1990).

[0036] A “sample,” such as a biological sample, is a sample obtained from a subject. As used herein, biological samples include all clinical samples useful for detection of a nucleic acids or proteins, including, but not limited to, cells, tissues, and bodily fluids, such as: blood; derivatives and fractions of blood, such as serum; urine; sputum; or CVS samples. Specific examples include peripheral blood mononuclear cells. A sample includes blood obtained from a human subject, such as whole blood or serum. Whole blood can be used in immunomonitoring methods described herein.

[0037] A “test sample” refers to a sample which can be used in the specific detection, quantitation, qualitative detection, characterization, or a combination thereof, of molecules contained therein. The molecule need not be in a purified form. Various other molecules can also be present with the test molecule. Purification or isolation of the test molecule, if needed,can be conducted by methods known to those in the art, such as by using a commercially available purification kit or the like.

[0038] By “contacting” is meant placement in direct physical association, for example solid, liquid or gaseous forms. Contacting includes, for example, direct physical association of fully- and partially solvated molecules.

[0039] As used herein, a “biomarker” is a characteristic that can be objectively measured and evaluated as an indicator of a normal biologic or pathogenic process or pharmacological response to a therapeutic intervention. Biomarker panels and their associated algorithms can encompass one or more analytes (e.g., proteins, nucleic acids, and metabolites), physical measurements, or combinations thereof. As used herein, an “analyte” is a substance or chemical constituent that can be objectively measured and determined in an analytical procedure such as immunoassay or mass spectrometry.

[0040] As used herein, “a reference expression level of a biomarker” refers to the expression level of a biomarker. This can refer to the expression level of a biomarker in a normal / healthy subject who has been determined by one skilled in the art using established methods as described herein to not be at risk of undesirable outcomes of pregnancy or postpartum, and / or a known expression level of a biomarker obtained from literature. It can also refer to the expression level of a biomarker of a subject who has had undesirable pregnancy or post-partum outcomes.

[0041] As used herein, “expression level of a biomarker” refers to the process by which a gene product is synthesized from a gene encoding the biomarker as known by those skilled in the art. The gene product can be, for example. RNA (ribonucleic acid) and protein. Expression level can be quantitatively measured by methods known by those skilled in the art such as, for example, northern blotting, amplification, polymerase chain reaction, microarray analysis, tag- based technologies (e.g., serial analysis of gene expression and next generation sequencing such as whole transcriptome shotgun sequencing or RNA-Seq). Western blotting, and combinations thereof.

[0042] “Signature” refers to the differential expression pattern when looking at gene expression or the presence of certain markers, such as immune markers. A signature may be exemplified by a particular set of biomarkers.

[0043] A “similarity value” is a number that represents the degree of similarity between two things being compared. For example, a similarity value may be a number that indicates theoverall similarity between a cell sample expression profile using specific phenotype-related biomarkers and a control specific to that template (for instance, the similarity to a “deregulated growth factor signaling pathway” template, where the phenotype is deregulated growth factor signaling pathway status). The similarity value may be expressed as a similarity metric, such as a correlation coefficient, or may simply be expressed as the expression level difference, or the aggregate of the expression level differences, between a cell sample expression profile and a baseline template.

[0044] As used herein, the terms “measuring expression levels,” “obtaining expression level,” and “detecting an expression level” and the like, includes methods that quantify a gene expression level of, for example, a transcript of a gene, or a protein encoded by a gene, as well as methods that determine whether a gene of interest is expressed at all. Thus, an assay which provides a “yes” or “no” result without necessarily providing quantification, of an amount of expression is an assay that “measures expression” as that term is used herein. Alternatively, a measured or obtained expression level may be expressed as any quantitative value, for example, a fold-change in expression, up or down, relative to a control gene or relative to the same gene in another sample, or a log ratio of expression, or any visual representation thereof, such as, for example, a “heatmap” where a color intensify is representative of the amount of gene expression detected. The genes identified as being differentially expressed may be used in a variety of nucleic acid or protein detection assays to detect or quantify the expression level of a gene or multiple genes in a given sample.

[0045] A “patient” can mean either a human or non-human animal, preferably a mammal.

[0046] As used herein, “subject”, as refers to an organism or to a cell sample, tissue sample or organ sample derived therefrom, including, for example, cultured cell lines, biopsy, blood sample, or fluid sample. The organism may be an animal, including but not limited to, an animal, such as a cow, a pig, a mouse, a rat, a chicken, a cat, a dog, etc., and is usually a mammal, such as a human.

[0047] As used herein, the term “pathway” is intended to mean a set of system components involved in two or more sequential molecular interactions that result in the production of a product or activity. A pathw ay can produce a variety of products or activities that can include, for example, intermolecular interactions, changes in expression of a nucleic acid or polypeptide, the formation or dissociation of a complex between two or more molecules, accumulation or destruction of a metabolic product, activation or deactivation of an enzyme orbinding activity. Thus, the term “pathway” includes a variety of pathway types, such as, for example, a biochemical pathway, a gene expression pathway, and a regulatory pathway. Similarly, a pathway can include a combination of these exemplary' pathway ty pes.

[0048] The terms “effective amount” or “therapeutically effective amount” refer to an amount sufficient to effect beneficial or desirable biological and / or clinical results.

[0049] An “immunoassay” (IA) is a biochemical test that measures the presence or concentration of a macromolecule or a small molecule in a solution through the use of an antibody or an antigen. The molecule detected by the immunoassay is often referred to as an "analyte" and is in many cases a protein, although it may be other kinds of molecules, of different sizes and types, as long as the proper antibodies that have the required properties for the assay are developed.

[0050] The term “analyte,” as used herein, refers to the substance to be detected, which maybe present in the sample (i. e. , the biological sample). The analyte can be any substance having a naturally occurring specific binding partner or for which a specific binding partner can be prepared. Thus, an analyte is a substance that can bind to one or more specific binding partners in an immunoassay.

[0051] The term “binding partner,” as used herein, is a member of a binding pair, i.e., a pair of molecules wherein one of the molecules binds to the second molecule. Binding partners that bind specifically to one another are termed “specific binding partners.” In addition to antigen and antibody binding partners commonly used in immunoassays, other specific binding partners include, for example, biotin and avidin, carbohydrates and lectins, complementary nucleotide sequences, effector and receptor molecules, cofactors and enzymes, enzyme inhibitors and enzymes, and the like. Furthermore, specific binding partners can include partner(s) that is / are analog(s) of the original specific binding partner, for example, an analyteanalog. Immunoreactive specific binding partners include antigens, antigen fragments, antibodies and antibody fragments, both monoclonal and polyclonal, and complexes thereof, including those formed by recombinant DNA methods.

[0052] As used interchangeably herein, the terms “epitope,” “epitopes” or “epitopes of interest” refer to a site(s) on any molecule that is recognized and is capable of binding to a complementary- site(s) on its specific binding partner. The molecule and specific binding partner are part of a specific binding pair. For example, an epitope can be a polypeptide, protein, hapten, carbohydrate antigen (such as, but not limited to, glycolipids, glycoproteins, orlipopolysaccharides) or polysaccharide and its specific binding partner, can be, but is not limited to, an antibody, which may be an autoantibody. Typically, an epitope is contained within a larger antigenic fragment (i.e., region or fragment capable of binding an antibody) and refers to the precise residues known to contact the specific binding partner. An antigenic fragment may contain more than one epitope.

[0053] As used herein, the terms ‘'specific binding,” “specificity” and '‘specifically binding”, characterize the interaction between two molecules having the ability to selectively react with one another as a pair (e.g., an antigen and antibody. The phrase “specifically binds to” refers for example to the ability of an antibody to specifically bind to its target antigen, while not specifically bind to other entities. Antibodies or antibody fragments that specifically bind to an analyte can be identified, for example, by diagnostic immunoassays (e.g., radioimmunoassay (“RIA”) and enzy me-linked immunosorbent assays (“ELISAs”) (See, for example, Paul, ed., Fundamental Immunology, 2nd ed., Raven Press, New York, pages 332-336 (1989)), surface plasmon resonance (e.g., sold under B1ACORE, Sweden), kinetic exclusion assay (e.g., sold under KINEXA, available from Sapidyne Instruments (Boise, Id.)) or other techniques known to those of skill in the art. The term “specifically binds” indicates that the binding preference (e.g., affinity) for the target molecule / sequence is at least 2-fold, more preferably at least 5- fold, and most preferably at least 10- or 20-fold over a non-specific target molecule (e.g. a randomly generated molecule lacking the specifically recognized site(s)).

[0054] A “solid phase,” as used herein, refers to any material that is insoluble, or can be made insoluble by a subsequent reaction. The solid phase can be chosen for its intrinsic ability' to attract and immobilize a capture agent. Alternatively, the solid phase can have affixed thereto a linking agent that has the ability to attract and immobilize the capture agent. The linking agent can, for example, include a charged substance that is oppositely charged with respect to the capture agent itself or to a charged substance conjugated to the capture agent. In general, the linking agent can be any binding partner (preferably specific) that is immobilized on (attached to) the solid phase and that has the ability to immobilize the capture agent through a binding reaction. The linking agent enables the indirect binding of the capture agent to a solid phase material before the performance of the assay or during the performance of the assay. The solid phase can, for example, be plastic, derivatized plastic, magnetic or non-magnetic metal, glass or silicon, including, for example, a test tube, microtiter well, sheet, bead, microparticle, chip, and other configurations known to those of ordinary skill in the art.

[0055] As used herein the term '‘detectable label” refers to any moiety that generates a measurable signal via optical, electrical, or other physical indication of a change of state of a molecule or molecules coupled to the moiety7. Such physical indicators encompass spectroscopic, photochemical, biochemical, immunochemical. electromagnetic, radiochemical, and chemical means, such as but not limited to fluorescence, chemifluorescence, chemiluminescence, and the like. As used with reference to a labeled detection agent, a “direct label” is a detectable label that is attached, by any means, to the detection agent. As used with reference to a labeled detection agent, an “indirect label” is a detectable label that specifically binds the detection agent. Thus, an indirect label includes a moiety that is the specific binding partner of a moiety of the detection agent. Biotin and avidin are examples of such moieties that are employed, for example, by contacting a biotinylated antibody with labeled avidin to produce an indirectly labeled antibody. An indicator reagent may be used to contact a detectable label to produce a detectable signal. Thus, for example, in conventional enzyme labeling, an antibody labeled with an enzy me can be contacted with a substrate (the indicator reagent) to produce a detectable signal, such as a colored reaction product.

[0056] As used herein, an “antibody” refers to a protein consisting of one or more polypeptides substantially encoded by immunoglobulin genes or fragments of immunoglobulin genes. This term encompasses polyclonal antibodies, monoclonal antibodies, and fragments thereof, as well as molecules engineered from immunoglobulin gene sequences. The recognized immunoglobulin genes include the kappa, lambda, alpha, gamma, delta, epsilon and mu constant region genes, as well as myriad immunoglobulin variable region genes. Light chains are classified as either kappa or lambda. Heavy chains are classified as gamma, mu, alpha, delta, or epsilon, which in turn define the immunoglobulin classes, IgG, IgM, IgA, IgD and IgE, respectively.

[0057] A typical immunoglobulin (antibody) structural unit is known to comprise a tetramer. Each tetramer is composed of two identical pairs of polypeptide chains, each pair having one “light” (about 25 kD) and one "heavy" chain (about 50-70 kD). The N-terminus of each chain defines a variable region of about 100 to 110 or more amino acids primarily responsible for antigen recognition. The terms “variable light chain (VL)” and “variable heavy chain (VH)” refer to these light and heavy7chains respectively.

[0058] Antibodies exist as intact immunoglobulins or as a number of well-characterized fragments produced by digestion with various peptidases. Thus, for example, pepsin digests an antibody below the disulfide linkages in the hinge region to produce F(ab')2, a dimer of Fab which itself is a light chain joined to VH-CHI by a disulfide bond. The F(ab')2 may be reduced under mild conditions to break the disulfide linkage in the hinge region thereby converting the (Fab')2 dimer into a Fab' monomer. The Fab' monomer is essentially a Fab with part of the hinge region (see, FUNDAMENTAL IMMUNOLOGY, W. E. Paul, ed., Raven Press, N.Y. (1993), for a more detailed description of other antibody fragments). While various antibody fragments are defined in terms of the digestion of an intact antibody, one of skill will appreciate that such Fab' fragments may be synthesized de novo either chemically or by utilizing recombinant DNA methodology.

[0059] Thus, the term “antibody,” as used herein also includes antibody fragments either produced by the modification of whole antibodies or synthesized de novo using recombinant DNA methodologies. Preferred antibodies include single chain antibodies (antibodies that exist as a single polypeptide chain), more preferably single chain Fv antibodies (sFv or scFv), in which a variable heavy and a variable light chain are joined together (directly or through a peptide linker) to form a continuous polypeptide. The single chain Fv antibody is a covalently linked VH-VL heterodimer which may be expressed from a nucleic acid including VH- and VL-en coding sequences either joined directly or joined by a peptide-encoding linker. Huston, et al. (1988) PROC. NAT. ACAD. SCI. USA, 85: 5879-5883. While the VH and VL are connected to each as a single polypeptide chain, the VH and VL domains associate non- covalently. The scFv antibodies and a number of other structures converting the naturally aggregated, but chemically separated, light and heavy polypeptide chains from an antibody V region into a molecule that folds into a three dimensional structure substantially similar to the structure of an antigen-binding site are know n to those of skill in the art.

[0060] The term “preterm birth” refers to a birth or delivery occurring at or after 20 0 / 7 weeks of gestation and before 37 0 / 7 weeks of gestation.

[0061] The term “spontaneous preterm birth” refers to a preterm birth following preterm labor, preterm premature rupture of membranes, or cervical insufficiency.

[0062] The term “preterm labor” also known as premature labor, refers to the beginning of regular contractions that cause the cervix to begin dilation and effacement before the 37th week of pregnancy.

[0063] The term “hypertensive disorder of pregnancy” refers to a systolic blood pressure of 140 mm Hg or more or a diastolic blood pressure of 90 mm Hg or more, or both, during pregnancy. Hypertensive disorders of pregnancy can be further classified into chronic hypertension, gestational hypertension, preeclampsia, eclampsia, preeclampsia superimposed on chronic hypertension. HELLP syndrome, or postpartum hypertension.

[0064] The term “chronic hypertension during pregnancy" refers to a systolic blood pressure of 140 mm Hg or more or a diastolic blood pressure of 90 mm Hg or more, or both, during pregnancy that is identified prior to 20 weeks of pregnancy and persists for longer than 12 weeks postpartum.

[0065] The term “pregnancy-induced hypertension” refers to a new onset hypertensive disorder that occurs among a pregnant individual. Pregnancy-induced hypertension includes but is not limited to gestational hypertension, preeclampsia, preeclampsia superimposed on chronic hypertension, eclampsia, postpartum hypertension, and hemolysis, elevated liver enzymes, low platelets (HELLP) syndrome.

[0066] The term “gestational hypertension” refers to a systolic blood pressure of 140 mm Hg or more or a diastolic blood pressure of 90 mm Hg or more, or both, on two occasions at least 4 hours apart after 20 weeks of gestation in an individual with a previously normal blood pressure.

[0067] The term “preeclampsia” refers to gestational hypertension with proteinuria or gestational hypertension with thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, or new-onset headache unresponsive to medication and not accounted for by alternative diagnoses or visual symptoms.

[0068] The term “eclampsia” refers to a generalized seizure in a patient with preeclampsia that cannot be attributed to other causes.

[0069] The term “preeclampsia superimposed upon chronic hypertension” refers to any of the following findings in a patient with chronic hypertension: a sudden increase in blood pressure that was previously well-controlled or an escalation of antihypertensive therapy to control blood pressure; new onset of proteinuria or a sudden increase in proteinuria in a patient with known proteinuria before or early in pregnancy; significant new7end-organ dysfunction consistent with preeclampsia after 20 weeks of gestation or postpartum.

[0070] The term '‘hemolysis, elevated liver enzymes, low platelets (HELLP) syndrome’’ refers to presence of the following findings in a pregnant or postpartum patient: hemolysis, elevated liver enz mes, and low platelets.

[0071] The term “postpartum hypertension” refers to either persistent or exacerbated hypertension, preeclampsia, or eclampsia in individuals with previous hypertensive disorders of pregnancy or a new onset of these conditions during the postpartum period.General Description of Invention

[0072] Until this invention, there has not been a biochemical measurement with sufficient predictive value to warrant recommendation for the detection of risk for preterm birth (PTB) or hypertensive disorders of pregnancy (HDPs). As such, in the U.S., an infant is bom preterm every 90 seconds; globally, every Vi second. The incidence of PTB exceeds that of many health conditions (e.g., cancer) yet PTB has received comparatively little attention (e.g., $240M in annual federal funding vs. $5,589M for cancer research). This is problematic considering that preterm-related illness increases risk for neonatal and infant death by > 15-fold, with survivors more likely to develop a myriad of chronic health conditions. U.S. acute care costs reach $85K- $6K per moderate-late preterm infant (birth 32-36 weeks) and $599K-$123K per extreme- early preterm infant (birth <32 weeks). Worldwide estimates for acute care range from $175K- $4K per PTB. The most recent comprehensive estimate of long-term financial burden was $26.2 billion.

[0073] It is well-established that, following critical illness and injury, inflammation and infection contribute to PTB and / or HDPs and are driven by aberrations in immune function that can be diagnosed through provocative testing of leukocyte immune responsiveness through stimulation of the toll-like receptor (TLR) pathways. Studies have shown that, during pregnancy, maternal leukocyte responsiveness is highly regulated.

[0074] The data presented herein (as found in Examples 1 and 2) shows that functional aberrations in maternal leukocyte responsiveness following TLR ligand binding contribute to spontaneous early birth. Further, DNA methylation, which involves the addition of methyl groups to DNA loci and can affect gene expression, clearly differs in various maternal and fetal tissues at the time of sPTB vs. full term birth, with some differentially methylated loci having potential implications for immune function.

[0075] To date, no studies have tested whether patterns of differential DNA methylation (DNAm) precede PTB and / or HDPs. The data disclosed herein identifies differentially methylated maternal leukocyte DNA loci months before PTB with potential implications for TLR signal transduction via dysregulation of AP-1, NFKB, and PI3K signaling. Given that PI3K activation can serve as a compensatory anti-inflammatory response when inflammation is enhanced via AP- 1 / NFKB up-regulation, an imbalance within these pathways can help to explain functional results. These findings are important in that progesterone, the only available sPTB preventive therapeutic, reduces AP-1 / NFKB activation via TLR4 as well as cytokine.

[0076] To date, no studies have combined data surrounding the regulation of immune responsiveness via concurrently assessed patterns of DNAm and mRNA expression of genes pertinent to regulation in the prediction of PTB and / or HDPs. Here, we applied a targeted, nested case-control design to identify genes with high biological plausibility that may be differentially expressed well in advance of sPTB. Framework: Two-hit hypothesis of inflammatory sPTB. In early pregnancy, we collected venous whole blood from 161 individuals. Here, we focus on the pre-COVID-19 arm (n=90) and report results from a comparison of those who progressed to sPTB (n= 12) versus spontaneous full-term birth (n= 12). We compared the expression of 770 gene transcripts across seven myeloid cell t pes by multiplex direct detection, using DESeq2 for analysis. We identified 45 gene transcripts with evidence of differential expression, defined as a p <0.10 (23 transcripts), log2 fold change >0.50 (6 transcripts), or both (16 transcripts). No transcripts withheld adjustment for multiple comparisons. The transcripts meeting both criteria for evaluation included CD209, COL4A1, TSPAN8, SPHK1. CEACAM8. SERPINB7, TNC, RAB20, ICAM1. PDGFA. SERPINE3, SMAD2, CXCL9, CTSL, ATF3, and PTGDS. Identified gene transcripts play diverse roles, contributing to cellular structure and function, signal transduction, cell adhesion, migration, proliferation, apoptosis, pathogen recognition and immune modulation.

[0077] As described in Examples 1 and 2, neuroendocrine and interleukin (IL)- 1 regulation in psychological stress-associated early birth was studied among low-risk women. Secondary analysis of an expanded set of eight cytokine production capacity markers were used to generate predictive models for subsequent birth timing. Disclosed herein is data showing that 30+2 week leukocyte cytokine production capacity (pro-inflammatory interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), interleukin- 1 beta (IL- 10), interleukin-8 (IL-8), macrophage inflammatory proteins (MIPs), monocyte chemoattractant proteins (MCPs), orinterleukin- 17 (IL-17); anti-inflammatory interleukin- 10 (IL-10), tumor necrosis factor receptor I (TNFRI), tumor necrosis factor receptor II (TNFRII), interferon-gamma (IFN-y), interleukin- 12p70 (IL-12p70), interleukin- 13 (IL-13), interleukin-37 (IL-37), transforming growth factors (TGFs), resolvins, or protectins) is associated with spontaneous birth at < 38 weeks (mildly early) with 58% sensitivity and 98% specificity (AUROC = 0.8. PPV = 78%, NPV = 94%, LR+ = 23.3 [very large risk increase], LR- = 0.4 [slight to moderate risk decrease]).

[0078] Example 2 shows DNA methylation markers of maternal immune responsiveness. In this example, frozen whole blood from the low-risk cohort described above, leukocyte DNA methylation was compared at 30+2 weeks among future sPTB cases (n = 5) vs. full term controls (n = 11), identifying 432 differentially methylated CpG loci (M > 0.4, p < .001; 274 linked to known genes). Ingenuity7Pathway Analysis showed that DNA loci of genes with implications for TLR4 signal transduction were differentially methylated, namely genes affecting AP-1 pro-inflammatory' signaling, NFKB pro-inflammatory signaling, and PI3K. antiinflammatory' signaling. It appears that some women are showing increased activation of AP- 1 and NFKB signaling with a net effect of hyper-inflammatory7responsiveness w hile others are showing PI3K compensatory7responses with a net effect of hypo-responsiveness.

[0079] 13 differentially regulated canonical pathways have been identified according to the differentially methylated loci (growth hormone signaling, CNTF Signaling, FLT3 signaling in hematopoietic progenitor cells, acute myeloid leukemia signaling, beta- alanine degradation I, HGF signaling, EGF signaling, 4-aminobutyrate degradation I, adipogenesis pathway, circadian rhythm signaling, ERK / MAPK signaling, protein citrullination, glutamate degradation II [via 4-aminobutyrate]).

[0080] A large cohort of higher-risk women (women with a prior history of sPTB are expected to experience sPTB at a rate of 30% for the assessed pregnancy), the ability7of markers of leukocyte responsiveness to predict subsequent sPTB risk, and the effectiveness of clinically prescribed progesterone on pregnancy prolongation can also be measured. This can allow for precise assessment and determination of the earliest point of risk detection using functional cytokine production capacity markers and molecular DNA methylation markers.

[0081] The first longitudinal data monitoring maternal leukocyte ex vivo TLR-mediated cytokine production capacity in the months PTB and HDPs in a high-risk cohort are collected.These data inform the development of biomarker strategy randomized controlled trials aimed at normalizing maternal immune function through precision interventions.

[0082] Functional epigenetic pathways are determined for increasing risk for PTB and HDPs, DNA methylation signatures are pursued in screening tool designs for patient stratification to available therapies (i.e., progesterone), as molecular targets for novel immunotherapies, or as surrogate endpoints for therapeutic response. Analyses allow for comparison associations among functional vs. molecular markers of immune responsiveness with PTB and HDPs.

[0083] The first data establishing baseline functional and molecular markers of immune responsiveness among progesterone recipients is carried out. If pre-treatment responsiveness predicts differential response to progesterone, clinical support tools are developed to stratify progesterone in high risk and current unknown risk populations, potentially expanding progesterone’s reach from 5% of pregnancies with limited effectiveness (currently effective among 1 / 3 of recipients) to an anticipated 11% of pregnancies with optimal effectiveness. This is tested through biomarker strategy randomized controlled trials of progesterone stratification. Therefore, with markers of PTB risk established, biochemical screening using this tool can be recommended for every pregnant woman (there are nearly 4 million births in the U.S. alone each year). Information regarding screen positive vs. screen negative status can direct clinicians to the need for more frequent assessments and high-risk care and can be capable of identifying the precise preventive intervention a screen positive woman would benefit from.

[0084] This approach is unique in that post tools of this nature do not differentiate between PTB and HDP phenotypes and both leukocyte responsive phenotypes, both of which contribute to PTB and HDPs. This has significant implications for optimizing treatment regimens.

[0085] Disclosed herein are methods of determining preterm birth (PTB) or hypertensive disorders of pregnancy (HDPs) in a pregnant subj ect, comprising: obtaining a biological sample from a pregnant subject; stimulating the blood sample with an immunogen; assaying Toll-like receptor (TLR)-mediated functional immune responsiveness after stimulation, wherein TLR- mediated immune responsiveness comprises at least one cytokine; detecting a level of at least one cytokine, wherein the cytokine comprises pro-inflammatory cytokines or antiinflammatory cytokines; and determining the pregnant subject is at an increased risk of PTB or HDP if the level of at least one cytokine is above a probability threshold level.

[0086] Also disclosed is a predictive algorithm to detect a PTB or HDP risk score in a subject, comprising: obtaining a biological sample from the subject; stimulating the blood sample withan immunogen; assaying Toll-like receptor (TLR)-mediated functional immune responsiveness after stimulation, wherein TLR-mediated immune responsiveness comprises at least one cytokine; detecting a level of at least one cytokine, wherein the cytokine comprises pro- inflammatory cytokine or anti-inflammatory cytokine; determining a probability threshold level using the level of at least one cytokine; and determining a risk category based on the probability threshold level, wherein the probability threshold level of <0.2 indicates a low-risk category, the probability threshold level of 0.2-0.4 indicates a moderate-risk category, the probability threshold level of >0.4 indicates a high-risk category, wherein the subject in high- risk category comprises elevated levels of at least one cytokine as compared to a control probability threshold level.

[0087] The subject can be any mammalian subject. In particularly preferred embodiments, the subject is a human female.

[0088] Both status of immune markers as well as methylation status can be used to diagnose increased likelihood of PTB and / or HDPs. These factors can be used individually or together. Additional factors can be considered as well. These are described in detail below.

[0089] For example, the markers can be upregulated or downregulated. By “upregulated” means that the marker is expressed or detected in 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%. 11%. 12%. 13%. 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%,26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%,42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%,58%, 59%, 60%, 61%, 62%. 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%,74%, 75%. 76%. 77%. 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%, or 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, or 10 times or more compared to a control.

[0090] By '’downregulated" means that the marker is expressed or detected 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%. 11%. 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%. 23%. 24%. 25%. 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%,38%, 39%, 40%, 41 %, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%,54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%,70%, 71%, 72%, 73%. 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,86%. 87%. 88%. 89%. 90%. 91%. 92%. 93%. 94%, 95%, 96%, 97%, 98%, 99%, or 100%, or1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, or 10 times or less compared to a control.

[0091] By “increased likelihood” is meant that the subject is 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%. 19%. 20%, 21%, 22%, 23%, 24%, 25%. 26%. 27%. 28%. 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%,41 %, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%,57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%,73%, 74%, 75%, 76%. 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,89%. 90%. 91%. 92%. 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% more likely to have PTB and / or HDPs. The subject can also be 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85. 86. 87. 88, 89, 90, 91, 92,93. 94. 95, 96, 97, 98, 99, or 100 or more likely to have a PTB and / or HDPs.

[0092] In some embodiments “preterm birth (PTB)” can be spontaneous preterm birth (sPTB). In some embodiments “hypertensive disorders of pregnancy” can comprise chronic hypertension, gestational hypertension, preeclampsia, superimposed preeclampsia on chronic hypertension, eclampsia, transient hypertension.

[0093] The immune markers can be TLR-mediated. TLR-mediated immune response can be measured by determining the status of TLR1 to TLR10. Examples of how to do this can be found in Example 3. The immune marker(s) can serve various roles, including as pro- inflammatory’ and / or anti-inflammatory mediators. For example, pro-inflammatory markers can be selected from the group comprising interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), interleukin-1 beta (IL- 10), interleukin-8 (IL-8), macrophage inflammatory proteins (MIPs), monocyte chemoattractant proteins (MCPs), or interleukin- 17 (IL- 17). Antiinflammatory markers can be selected from the group comprising interleukin- 10 (IL- 10), tumor necrosis factor receptor I (TNFRI). tumor necrosis factor receptor II (TNFRII), interferongamma (IFN-y), interleukin- 12p70 (IL-12p70), interleukin- 13 (IL-13), interleukin-37 (IL-37), transforming growth factors (TGFs), resolvins, and / or protectins. Immune markers reflective of Thl- (IL-6, TNF-a, IL-1|3, IFN-y, IL-12p70) vs. Th2-type (IL-10) cytokines, the Type II IFN response (IFN-y), and crosstalk between the innate and adaptive immune systems (IFN-y, IL-12p70) can also be selected.

[0094] In some embodiments, the immune markers reflective of TH1- vs TH2- type cytokine balance are selected from the group comprising IL-6, TNF-a, IL-ip, IFN-y, IL-1, IL-4, IL-5, IL- 13, IL- 12, and / or IL- 10. In some embodiments, the immune markers indicative of the interferon response are selected from the group comprising IFNs. In some embodiments, the immune markers involved in the crosstalk between the innate and adaptive immune systems are selected from the group comprising IFNs, IL-1 , IL-6, TNF-a, IL-10, IL-12, MCPs, IPs, IL- 15, and / or IL-23.

[0095] Classical laboratory' values must also be considered in the algorithm, including white blood cell, red blood cell, and platelet counts.

[0096] The subject can be identified as having increased likelihood of PTB and / or HDPs is subsequently treated to prevent PTB and / or HDPs, or monitored more frequently than a subject who has not been identified as having an increased likelihood of having a PTB and / or HDPs, or counseled to modify behavior to reduce risk of PTB and / or HDPs.

[0097] In further embodiments, the subject can be any subject in a population at risk for PTB and / or HDPs. For instance, the subject can be a human female of reproductive age at risk for spontaneous abortion. In particular embodiments, the subject can be a human female greater than 35 years of age, greater than 40 years of age or greater than 45 years of age. In other particular embodiments, the subject can be a human female less than 20 years of age or less than 15 years of age. However, essentially a woman of reproductive age, is a candidate for obtaining the materials and methods of the instant invention.

[0098] The subject can be measured before pregnancy, after pregnancy, or during pregnancy, or at multiple time points, including before, during and / or after pregnancy. When monitored during pregnancy, the subject can be anywhere from 1 day to 42 weeks pregnant. In one embodiment, the subject is less than 20 weeks pregnant when assaying markers is carried out. In another embodiment, the subject is 30-32 weeks pregnant when assaying markers is carried out.Determination of Risk Score

[0099] Methods of determining an increased risk of PTB and / orHDPs can include calculating a risk score. This risk score can comprise cutoff values, which can be used to calculate a score. The score can then be used to determine whether the subject has an increased risk of adverse pregnancy outcome, or to determine a classification or “level” of risk. One of skill in the art will appreciate how to determine appropriate cutoff values.

[0100] For example, various cutoff values can be used to determine a subject’s risk likelihood of having, or developing, PTB and / or HDPs. These scores can comprise multiple cutoff values, which can then be used to place the subject in a risk category. By way of specific example, a risk score of 1-100 can be used to assess the probability of a subject having PTB and / or HDPs. It is noted that these values are exemplary only, and a person of skill in the art could readily determine appropriate risk scores, cutoff values, and risk categories.

[0101] Turning again to the example of a risk score of 1-100, a score of 100 can indicate the highest level of probability of a subject having an increased risk of PTB and / or HDPs, while a score of 0 can mean that the subject is highly unlikely to have PTB and / or HDPs. These risk scores can be used to determine cutoff values, which can then be used to place subjects into risk categories (or levels). Again, by way of example, a score of 25 or less (the first cutoff value) can mean that the subject is not likely to have PTB and / or HDPs, and does not require further monitoring, or requires less frequent monitoring than a subject with a higher score. This risk category is therefore considered “low risk.” A score from 25-75 (75 being the next cutoff level) can mean that the subject has an increased risk of having or developing a PTB and / or HDPs. This risk category can be considered “moderate risk.” A score of higher than 75 (the third cutoff level) can mean that the subject is likely to have or develop an increased risk of PTB and / or HDPs. and this risk category is considered “high risk.” These categories can then be used to inform treatment, and the type of treatment can differ from subjects without a high risk of PTB and / or HDPs. Again, the risk levels given herein are exemplary only, as one of skill in the art can readily identify appropriate risk levels and cutoff values.

[0102] In a preferred embodiment, the control or baseline levels of immune markers are collected from “matched individuals.” According to the present invention, the phrase “matched individuals” refers to a matching of the control individuals on the basis of one or more characteristics, such as gender, age, race, or any relevant biological or sociological factor that may affect the baseline of the control individuals and the patient (e.g., preexisting conditions, consumption of particular substances, levels of other biological or physiological factors). The number of matched individuals from whom control samples must be obtained to establish a suitable control level (e.g., a population) can be determined by those of skill in the art, but should be statistically appropriate to establish a suitable baseline for comparison with the patient to be evaluated (i.e., the test patient). The values obtained from the control samples are statistically processed using any suitable method of statistical analysis to establish a suitablebaseline level using methods standard in the art for establishing such values. It will be appreciated by those of skill in the art that a baseline need not be established for each assay as the assay is performed but rather, a baseline can be established by referring to a form of stored information regarding a previously determined control level of gene expression. Such a form of stored information can include, for example, but is not limited to. a reference chart, listing or electronic file of population or individual data regarding “normal” (negative control) or positive gene expression; a medical chart for the patient recording data from previous evaluations; or any other source of data regarding control gene expression that is useful for the patient to be diagnosed or evaluated.

[0103] Additional factors can be used to determine an increased risk of PTB and / or HDPs. These can include, but are not limited to, lifestyle-associated risks, family history, previous PTB and / or HDPs by the subject, or medical conditions or genetic factors. In further embodiments, the subject can also be in any other population at risk for PTB and / or HDPs as determined by a practitioner of skill in the art.Methods of Detection of Immune Markers and / or Methylation Status

[0104] In some embodiments, a number of methods are utilized to measure, detect, determine, identity', and characterize the methylation status / level of a gene or a biomarker (e.g., CpG island-containing region / fragment) in identifying a subject as having an increased risk of PTB and / or HDPs.

[0105] In some embodiments, a biomarker (or an epigenetic marker) is methylated or unmethylated in a normal sample (e g., normal or control tissue without disease, or normal or control body fluid, stool, blood, serum, amniotic fluid), most importantly in healthy stool, blood, serum, amniotic fluid or other body fluid. In other embodiments, a biomarker (or an epigenetic marker) is hypomethylated or hypermethylated in a sample from a patient having or at risk of an increased risk of PTB and / or HDPs (e.g., one or more indications described herein); for example, at a decreased or increased (respectively) methylation frequency of at least about 50%. at least about 60%, at least about 70%. at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or about 100% in comparison to a normal sample. In one embodiment, a sample is also hypomethylated or hypermethylated in comparison to a previously obtained sample analysis of the same patient having or at risk of an increased risk of PTB and / or HDPs.

[0106] In some embodiments, DNA (e.g., genomic DNA such as extracted genomic DNA or treated genomic DNA) is isolated by any means standard in the art, including the use of commercially available kits. Briefly, wherein the DNA of interest is encapsulated by a cellular membrane the biological sample is disrupted and lysed by enzymatic, chemical or mechanical means. In some cases, the DNA solution is then cleared of proteins and other contaminants e.g., by digestion with proteinase K. The DNA is then recovered from the solution. In such cases, this is carried out by means of a variety of methods including salting out, organic extraction or binding of the DNA to a solid phase support. In some instances, the choice of method is affected by several factors including time, expense and required quantity of DNA.

[0107] Wherein the sample DNA is not enclosed in a membrane (e.g. circulating DNA from a cell free sample such as blood or urine) methods standard in the art for the isolation and / or purification of DNA are optionally employed (See, for example, Bettegowda et al. Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies. Sci. Transl. Med, 6(224): ra24. 2014). Such methods include the use of a protein degenerating reagent e.g. chaotropic salt e.g. guanidine hydrochloride or urea; or a detergent e.g. sodium dodecyl sulphate (SDS), cyanogen bromide. Alternative methods include but are not limited to ethanol precipitation or propanol precipitation, vacuum concentration amongst others by means of a centrifuge. In some cases, the person skilled in the art also makes use of devices such as filter devices e g. ultrafiltration, silica surfaces or membranes, magnetic particles, polystyrol particles, polystyrol surfaces, positively charged surfaces, and positively charged membranes, charged membranes, charged surfaces, charged switch membranes, and charged switched surfaces.

[0108] In some instances, once the nucleic acids have been extracted, methylation analysis is carried out by any means known in the art. A variety of methylation analysis procedures is known in the art and may be used to practice the methods disclosed herein. These assays allow7for determination of the methylation state of one or a plurality of CpG sites within a sample. In addition, these methods may be used for absolute or relative quantification of methylated nucleic acids. Such methylation assays involve, among other techniques, two major steps. The first step is a methylation specific reaction or separation, such as (i) bisulfite treatment, (ii) methylation specific binding, or (iii) methylation specific restriction enzymes. The second major step involves (i) amplification and detection, or (ii) direct detection, by a variety of methods such as (a) PCR (sequence-specific amplification) such as Taqman(R), (b) DNAsequencing of untreated and bisulfite-treated DNA, (c) sequencing by ligation of dye-modified probes (including cyclic ligation and cleavage), (d) pyrosequencing, (e) single-molecule sequencing, (f) mass spectroscopy, or (g) Southern blot analysis.

[0109] Additionally, restriction enzyme digestion of PCR products amplified from bisulfite- converted DNA may be used, e.g., the method described by Sadri and Hornsby (1996, Nucl. Acids Res. 24:5058-5059), or COBRA (Combined Bisulfite Restriction Analysis) (Xiong and Laird, 1997, Nucleic Acids Res. 25:2532-2534). COBRA analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific gene loci in small amounts of genomic DNA. Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of sodium bisulfite-treated DNA. Methylationdependent sequence differences are first introduced into the genomic DNA by standard bisulfite treatment according to the procedure described by Frommer et al. (Frommer et al, 1992, Proc. Nat. Acad. Sci. USA, 89, 1827-1831). PCR amplification of the bisulfite converted DNA is then performed using primers specific to the CpG sites of interest, followed by restriction endonuclease digestion, gel electrophoresis, and detection using specific, labeled hybridization probes. Methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR products in a linearly quantitative fashion across a wide spectrum of DNA methylation levels. In addition, this technique can be reliably applied to DNA obtained from micro-dissected paraffin-embedded tissue samples. Typical reagents (e.g., as might be found in a typical COBRA-based kit) for COBRA analysis may include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); restriction enzyme and appropriate buffer; gene-hybridization oligo; control hybridization oligo; kinase labeling kit for oligo probe; and radioactive nucleotides. Additionally, bisulfite conversion reagents may include DNA denaturation buffer; sulfo nation buffer; DNA recovery' reagents or kits (e.g., precipitation, ultrafiltration, affinity' column); desulfonation buffer; and DNA recovery components.

[0110] In an embodiment, the methylation profile of selected CpG sites is determined using methylation-Specific PCR (MSP). MSP allows for assessing the methylation status of virtually any group of CpG sites within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al, 1996, Proc. Nat. Acad. Sci. USA, 93, 9821-9826; U.S. Pat. Nos. 5.786,146, 6.017,704, 6.200,756, 6.265,171 (Herman and Baylin); U.S. Pat. Pub. No. 2010 / 0144836 (Van Engeland et al); which are hereby incorporated by reference in theirentirety). Briefly, DNA is modified by a deaminating agent such as sodium bisulfite to convert unmethylated, but not methylated cytosines to uracil, and subsequently amplified with primers specific for methylated versus unmethylated DNA. In some instances, typical reagents (e.g., as might be found in a typical MSP-based kit) for MSP analysis include, but are not limited to methylated and unmethylated PCR primers for specific gene (or methylation-altered DNA sequence or CpG island), optimized PCR buffers and deoxynucleotides, and specific probes. One may use quantitative multiplexed methylation specific PCR (QM-PCR), as described by Fackler et al. Fackler et al. 2004, Cancer Res. 64(13) 4442-4452; or Fackler et al, 2006, Clin. Cancer Res. 12(11 Pt 1) 3306-3310.

[0111] In an embodiment, the methylation profile of selected CpG sites is determined using MethyLight and / or Heavy Methyl Methods. The MethyLight and Heavy Methyl assays are a high-throughput quantitative methylation assay that utilizes fluorescence-based real-time PCR (Taq Man(R)) technology that requires no further manipulations after the PCR step (Eads, C. A. et al, 2000, Nucleic Acid Res. 28, e 32; Cottrell et al. 2007, J. Urology 177, 1753. U.S. Pat. No. 6,331,393 (Laird et al), the contents of which are hereby incorporated by reference in their entirety). Briefly, the MethyLight process begins with a mixed sample of genomic DNA that is converted, in a sodium bisulfite reaction, to a mixed pool of methylation-dependent sequence differences according to standard procedures (the bisulfite process converts unmethylated cytosine residues to uracil). Fluorescence-based PCR is then performed either in an '‘unbiased” (with primers that do not overlap known CpG methylation sites) PCR reaction, or in a “biased” (with PCR primers that overlap known CpG dinucleotides) reaction. In some cases, sequence discrimination occurs either at the level of the amplification process or at the level of the fluorescence detection process, or both. In some cases, the MethyLight assay is used as a quantitative test for methylation patterns in the genomic DNA sample, wherein sequence discrimination occurs at the level of probe hybridization. In this quantitative version, the PCR reaction provides for unbiased amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing of the biased PCR pool with either control oligonucleotides that do not “cover” known methylation sites (a fluorescence-based version of the “MSP” technique), or with oligonucleotides covering potential methylation sites. Typical reagents (e.g., as might be foundin atypical Methy Light-based kit) for Methy Light analysis may include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); TaqMan(R) probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase.

[0112] Quantitative MethyLight uses bisulfite to convert genomic DNA, and the methylated sites are amplified using PCR with methylation independent primers. Detection probes specific to the methylated and unmethylated sites with two different fluorophores provide simultaneous quantitative measurement of the methylation. The Heavy Methyl technique begins with bisulfate conversion of DNA. Next specific blockers prevent the amplification of unmethylated DNA. Methylated genomic DNA does not bind the blockers and their sequences will be amplified. The amplified sequences are detected with a methylation specific probe. (Cottrell et al, 2004, Nuc. Acids Res. 32:el0, the contents of which are hereby incorporated by reference in its entirety).

[0113] The Ms-SNuPE technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA. followed by singlenucleotide primer extension (Gonzalgo and Jones, 1997, Nucleic Acids Res. 25, 2529-2531). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific to bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site(s) of interest. In some cases, small amounts of DNA are analyzed (e.g., micro-dissected pathology sections), and the method avoids utilization of restriction enzymes for determining the methylation status at CpG sites. Typical reagents (e.g., as is found in a typical Ms-SNuPE-based kit) for Ms- SNuPE analysis include, but are not limited to: PCR primers for specific gene (or methylation- altered DNA sequence or CpG island); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE primers for specific gene; reaction buffer (for the Ms-SNuPE reaction); and radioactive nucleotides. Additionally, bisulfite conversion reagents may include DNA denaturation buffer; sulfonation buffer; DNA recovery regents or kit (e g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.

[0114] In another embodiment, the methylation status of selected CpG sites is determined using differential Binding-based Methylation Detection Methods. For identification of differentially methylated regions, one approach is to capture methylated DNA. This approachuses a protein, in which the methyl binding domain of MBD2 is fused to the Fc fragment of an antibody (MBD-FC) (Gebhard et al, 2006, Cancer Res. 66:6118-6128; and PCT Pub. No. WO 2006 / 056480 A2 (Relhi), the contents of which are hereby incorporated by reference in their entirety). This fusion protein has several advantages over conventional methylation specific antibodies. The MBD FC has a higher affinity to methylated DNA and it binds double stranded DNA. Most importantly the two proteins differ in the way they bind DNA. Methylation specific antibodies bind DNA stochastically, which means that only a binary answer can be obtained. The methyl binding domain of MBD-FC, on the other hand, binds DNA molecules regardless of their methylation status. The strength of this protein — DNA interaction is defined by the level of DNA methylation. After binding genomic DNA, eluate solutions of increasing salt concentrations can be used to fractionate non-methylated and methylated DNA allowing for a more controlled separation (Gebhard et al, 2006, Nucleic Acids Res. 34: e82). Consequently, this method, called Methyl-CpG immunoprecipitation (MCIP), not only enriches, but also fractionates genomic DNA according to methylation level, which is particularly helpful when the unmethylated DNA fraction should be investigated as well.

[0115] In an alternative embodiment, a 5-methyl cytidine antibody to bind and precipitate methylated DNA. Antibodies are available from Abeam (Cambridge, Mass.), Diagenode (Sparta, N.J.) or Eurogentec (c / o AnaSpec, Fremont. Calif). Once the methylated fragments have been separated, they may be sequenced using microarray based techniques such as methylated CpG-island recovery assay (MIRA) or methylated DNA immunoprecipitation (MeDIP) (Pelizzola et al, 2008, Genome Res. 18, 1652-1659; O'Geen et al, 2006, BioTechniques 41(5), 577-580, Weber et al, 2005. Nat. Genet. 37. 853-862; Horak and Snyder, 2002, Methods Enzymol, 350, 469-83; Lieb, 2003, Methods Mol Biol, 224, 99-109). Another technique is methyl-CpG binding domain column / segregation of partly melted molecules (MBD / SPM, Shiraishi et al, 1999, Proc. Natl. Acad. Sci. USA 96(6):2913-2918).

[0116] In some embodiments, methods for detecting methylation include randomly shearing or randomly fragmenting the genomic DNA, cutting the DNA with a methylation-dependent or methylation-sensitive restriction enzyme and subsequently selectively identifying and / or analyzing the cut or uncut DNA. Selective identification can include, for example, separating cut and uncut DNA (e.g., by size) and quantifying a sequence of interest that was cut or, alternatively, that was not cut. See. e.g., U.S. Pat. No. 7,186.512. Alternatively, the method can encompass amplify ing intact DNA after restriction enz me digestion, thereby only amplifyingDNA that was not cleaved by the restriction enzyme in the area amplified. See, e.g., U.S. Pat. Nos. 7,910,296; 7,901,880; and 7,459,274. In some embodiments, amplification can be performed using primers that are gene specific.

[0117] For example, there are methyl-sensitive enzymes that preferentially or substantially cleave or digest at their DNA recognition sequence if it is non-methylated. Thus, an unmethylated DNA sample is cut into smaller fragments than a methylated DNA sample. Similarly, a hypermethylated DNA sample is not cleaved. In contrast, there are methylsensitive enzymes that cleave at their DNA recognition sequence only if it is methylated. Methyl-sensitive enzymes that digest unmethylated DNA suitable for use in methods of the technology include, but are not limited to, Hpall, Hhal, Maell, BstUI and Acil. In some instances, an enzyme that is used is Hpall that cuts only the unmethylated sequence CCGG. In other instances, another enzyme that is used is Hhal that cuts only the unmethylated sequence GCGC. Both enzymes are available from New England BioLabs(R), Inc. Combinations of two or more methyl-sensitive enzymes that digest only unmethylated DNA are also used. Suitable enzymes that digest only methylated DNA include, but are not limited to, Dpnl, which only cuts at fully methylated 5'-GATC sequences, and McrBC, an endonuclease, which cuts DNA containing modified cytosines (5 -methylcytosine or 5-hydroxymethylcytosine or N4- methylcytosine) and cuts at recognition site 5' PumC(N4o-3ooo) PumC 3' (New England BioLabs, Inc., Beverly, Mass.). Cleavage methods and procedures for selected restriction enzymes for cutting DNA at specific sites are well known to the skilled artisan. For example, many suppliers of restriction enzy mes provide information on conditions and types of DNA sequences cut by specific restriction enzymes, including New England BioLabs, Promega Biochems, Boehringer-Mannheim, and the like. Sambrook et al. (See Sambrook et al. Molecular Biology: A Laboratory Approach, Cold Spring Harbor, N.Y. 1989) provide a general description of methods for using restriction enzymes and other enzymes.

[0118] In some instances, a methylation-dependent restriction enzyme is a restriction enzyme that cleaves or digests DNA at or in proximity to a methylated recognition sequence, but does not cleave DNA at or near the same sequence when the recognition sequence is not methylated. Methylation-dependent restriction enzymes include those that cut at a methylated recognition sequence (e.g.. Dpnl) and enzymes that cut at a sequence near but not at the recognition sequence (e.g.. McrBC). For example, McrBCs recognition sequence is 5’ RmC (N40-3000) RmC 3 where “R” is a purine and ‘’mC” is a methylated cytosine and ‘'N40-3000” indicates thedistance between the two RmC half sites for which a restriction event has been observed. McrBC generally cuts close to one half-site or the other, but cleavage positions are typically distributed over several base pairs, approximately 30 base pairs from the methylated base. McrBC sometimes cuts 3' of both half sites, sometimes 5' of both half sites, and sometimes between the two sites. Exemplary methylation-dependent restriction enzymes include, e.g., McrBC, McrA, MrrA, Bisl, Glal and Dpnl. One of skill in the art will appreciate that any methylation-dependent restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use with one or more methods described herein.

[0119] In some cases, a methylation-sensitive restriction enzyme is a restriction enzyme that cleaves DNA at or in proximity to an unmethylated recognition sequence but does not cleave at or in proximity to the same sequence when the recognition sequence is methylated. Exemplary methylation-sensitive restriction enzymes are described in, e.g., McClelland et al, 22(17) NUCLEIC ACIDS RES. 3640-59 (1994). Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when a cytosine within the recognition sequence is methylated at position C5 include, e.g., Aat II, Aci I, Acd I, Age I, Alu I, Asc I, Ase I, AsiS I, Bbe I, BsaA I, BsaH I, BsiE I, BsiW I, BsrF I, BssH II, BssK I, BstB I, BstN I, BstU I, Cla I, Eae I, Eag I, Fau I, Fse I, Hha I, HinPl I, HinC II, Hpa II, Hpy99 I. HpyCH4 IV. Kas I, Mbo I, Mlu I, MapAl I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, Sap I, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and Zra I. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when an adenosine within the recognition sequence is methylated at position N6 include, e.g., Mbo I. One of skill in the art will appreciate that any methylationsensitive restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use with one or more of the methods described herein. One of skill in the art will further appreciate that a methylation-sensitive restriction enzy me that fails to cut in the presence of methylation of a cytosine at or near its recognition sequence may be insensitive to the presence of methylation of an adenosine at or near its recognition sequence. Likewise, a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of an adenosine at or near its recognition sequence may be insensitive to the presence of methylation of a cytosine at or near its recognition sequence. For example, Sau3AI is sensitive (i.e., fails to cut) to the presence of a methylated cytosine at or near its recognition sequence, but is insensitive (i.e., cuts) to the presence of a methylated adenosine at or near itsrecognition sequence. One of skill in the art will also appreciate that some methylationsensitive restriction enzymes are blocked by methylation of bases on one or both strands of DNA encompassing of their recognition sequence, while other methylation-sensitive restriction enzymes are blocked only by methylation on both strands, but can cut if a recognition site is hemi-methylated.

[0120] In alternative embodiments, adaptors are optionally added to the ends of the randomly fragmented DNA, the DNA is then digested with a methylation-dependent or methylationsensitive restriction enzy me, and intact DNA is subsequently amplified using primers that hybridize to the adaptor sequences. In this case, a second step is performed to determine the presence, absence or quantity of a particular gene in an amplified pool of DNA. In some embodiments, the DNA is amplified using real-time, quantitative PCR.

[0121] In other embodiments, the methods comprise quantifying the average methylation density in a target sequence within a population of genomic DNA. In some embodiments, the method comprises contacting genomic DNA with a methy lation-dependent restriction enzyme or methylation-sensitive restriction enzyme under conditions that allow for at least some copies of potential restriction enzy me cleavage sites in the locus to remain uncleaved; quantifying intact copies of the locus; and comparing the quantify of amplified product to a control value representing the quantify of methylation of control DNA, thereby quantifying the average methylation densify in the locus compared to the methylation densify of the control DNA.

[0122] In some instances, the quantify of methylation of a locus of DNA is determined by providing a sample of genomic DNA comprising the locus, cleaving the DNA with a restriction enzyme that is either methylation-sensitive or methylation-dependent, and then quantifying the amount of intact DNA or quantifying the amount of cut DNA at the DNA locus of interest. The amount of intact or cut DNA will depend on the initial amount of genomic DNA containing the locus, the amount of methylation in the locus, and the number (i.e., the fraction) of nucleotides in the locus that are methylated in the genomic DNA. The amount of methylation in a DNA locus can be determined by comparing the quantify of intact DNA or cutting DNA to a control value representing the quantify of intact DNA or cut DNA in a similarly-treated DNA sample. The control value can represent a known or predicted number of methylated nucleotides. Alternatively, the control value can represent the quantify of intact or cut DNA from the same locus in another (e.g.. normal, non-diseased) cell or a second locus.

[0123] By using at least one methylation-sensitive or methylation-dependent restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved and subsequently quantifying the remaining intact copies and comparing the quantity to a control, average methylation density of a locus can be determined. If the methylation-sensitive restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be directly proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Similarly, if a methylationdependent restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be inversely proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Such assays are disclosed in. e.g., U.S. Pat. No. 7.910,296.

[0124] The methylated CpG island amplification (MCA) technique is a method that can be used to screen for altered methylation patterns in genomic DNA, and to isolate specific sequences associated with these changes (Toyota et al, 1999, Cancer Res. 59, 2307-2312, U.S. Pat. No. 7,700.324 (Issa et al), the contents of which are hereby incorporated by reference in their entirety). Briefly, restriction enzymes with different sensitivities to cytosine methylation in their recognition sites are used to digest genomic DNAs from primary' tumors, cell lines, and normal tissues prior to arbitrarily primed PCR amplification. Fragments that show differential methylation are cloned and sequenced after resolving the PCR products on high-resolution polyacrylamide gels. The cloned fragments are then used as probes for Southern analysis to confirm differential methylation of these regions. Typical reagents (e.g., as might be found in a ty pical MCA-based kit) for MCA analysis may include, but are not limited to: PCR primers for arbitrary priming Genomic DNA; PCR buffers and nucleotides, restriction enzymes and appropriate buffers; gene-hybridization oligos or probes; control hybridization oligos or probes.

[0125] Additional methylation detection methods include those methods described in, e.g., U.S. Pat. Nos. 7,553.627; 6,331,393; U.S. patent Ser. No. 12 / 476,981; U.S. Patent Publication No. 2005 / 0069879; Rein, et al, 26(10) NUCLEIC ACIDS RES. 2255-64 (1998); and Olek et al, 17(3) NAT. GENET. 275-6 (1997).

[0126] In another embodiment, the methylation status of selected CpG sites is determined using Methylation-Sensitive High-Resolution Melting (HRM). Recently, Wojdacz et al. reported methylation-sensitive high-resolution melting as a technique to assess methylation. (Wojdacz and Dobrovic, 2007, Nuc. Acids Res. 35(6) e41; Wojdacz et al. 2008, Nat. Prot. 3(12) 1903-1908; Bahc et al, 2009 J. Mol. Diagn. 11 102-108; and US Pat. Pub. No. 2009 / 01 5791 (Wojdacz et al), the contents of which are hereby incorporated by reference in their entirety). A variety of commercially available real time PCR machines have HRM systems including the Roche LightCycler480, Corbett Research RotorGene6000. and the Applied Biosystems 7500. HRM may also be combined with other amplification techniques such as pyrosequencing as described by Candiloro et al. (Candiloro et al, 2011, Epigenetics 6(4) 500-507).

[0127] In another embodiment, the methylation status of selected CpG locus is determined using a primer extension assay, including an optimized PCR amplification reaction that produces amplified targets for analysis using mass spectrometry. The assay can also be done in multiplex. Mass spectrometry is a particularly effective method for the detection of polynucleotides associated with the differentially methylated regulator ' elements. The presence of the polynucleotide sequence is verified by comparing the mass of the detected signal with the expected mass of the polynucleotide of interest. The relative signal strength, e.g., mass peak on a spectrum, for a particular polynucleotide sequence indicates the relative population of a specific allele, thus enabling calculation of the allele ratio directly from the data. This method is described in detail in PCT Pub. No. WO 2005 / 012578A1 (Beaulieu et al), which is hereby incorporated by reference in its entirety. For methylation analysis, the assay can be adopted to detect bisulfite introduced methylation dependent C to T sequence changes. These methods are particularly useful for performing multiplexed amplification reactions and multiplexed primer extension reactions (e.g., multiplexed homogeneous primer mass extension (hME) assays) in a single well to further increase the throughput and reduce the cost per reaction for primer extension reactions.

[0128] Other methods for DNA methylation analysis include restriction landmark genomic scanning (RLGS, Costello et al, 2002, Meth. Mol Biol, 200, 53-70), methylation-sensitive- representational difference analysis (MS-RD A, Ushijima and Yamashita, 2009, Methods Mol Biol 507, 1 17-130). Comprehensive high-throughput arrays for relative methylation (CHARM) techniques are described in WO 2009 / 021141 (Feinberg and Irizarry')- TheRoche(R) NimbleGen(R) microarrays including the Chromatin Immunoprecipitation-on-chip (ChlP-chip) or methylated DNA immunoprecipitation-on-chip (MeDIP-chip). Others have reported bisulfate conversion, padlock probe hybridization, circularization, amplification and next generation or multiplexed sequencing for high throughput detection of methylation (Deng et al, 2009. Nat. Biotechnol 27, 353-360; Ball et al, 2009, Nat. Biotechnol 27. 361-368; U.S. Pat. No. 7,61 1,869 (Fan)). As an alternative to bisulfate oxidation, Bayeyt et al. have reported selective oxidants that oxidize 5-methylcytosine, without reacting with thymidine, which are followed by PCR or pyro sequencing (WO 2009 / 049916 (Bayeyt et al). These references for these techniques are hereby incorporated by reference in their entirety’.

[0129] In some instances, quantitative amplification methods (e.g., quantitative PCR or quantitative linear amplification) are used to quantify the amount of intact DNA within a locus flanked by amplification primers following restriction digestion. Methods of quantitative amplification are disclosed in, e.g.. U.S. Pat. Nos. 6,180.349; 6,033,854; and 5,972,602, as well as in. e.g., DeGraves, et al, 34(1) BIOTECHNIQUES 106-15 (2003); Deiman B, et al., 20(2) MOL. BIOTECHNOL. 163-79 (2002); and Gibson et al, 6 GENOME RESEARCH 995-1001 (1996).

[0130] Following reaction or separation of nucleic acid in a methylation specific manner, the nucleic acid in some cases are subjected to sequence-based analysis. For example, once it is determined that one particular genomic sequence from a sample is hypermethylated or hypomethylated compared to its counterpart, the amount of this genomic sequence can be determined. Subsequently, this amount can be compared to a standard control value and used to determine the present of liver cancer in the sample. In many instances, it is desirable to amplify' a nucleic acid sequence using any of several nucleic acid amplification procedures which are well known in the art. Specifically, nucleic acid amplification is the chemical or enzymatic synthesis of nucleic acid copies which contain a sequence that is complementary’ to a nucleic acid sequence being amplified (template). The methods and kits may use any nucleic acid amplification or detection methods known to one skilled in the art, such as those described in U.S. Pat. No. 5,525,462 (Takarada et al); U.S. Pat. No. 6,114,117 (Hepp et al); U.S. Pat. No. 6,127,120 (Graham et al); U.S. Pat. No. 6,344,317 (Umovitz); U.S. Pat. No. 6,448,001 (Oku); U.S. Pat. No. 6,528,632 (Catanzariti et al); and PCT Pub. No. WO 2005 / 111209 (Nakajima et al); all of which are incorporated herein by reference in their entirety.

[0131] In some embodiments, nucleic acids are amplified by PCR amplification using methodologies known to one skilled in the art. One skilled in the art will recognize, however, that amplification can be accomplished by any known method, such as ligase chain reaction (LCR), Q-replicas amplification, rolling circle amplification, transcription amplification, selfsustained sequence replication, nucleic acid sequence-based amplification (NASBA), each of which provides sufficient amplification. Branched-DNA technology is also optionally used to qualitatively demonstrate the presence of a sequence of the technology7, which represents a particular methylation pattern, or to quantitatively determine the amount of this particular genomic sequence in a sample. Nolte reviews branched-DNA signal amplification for direct quantitation of nucleic acid sequences in clinical samples (Nolte, 1998, Adv. Clin. Chem. 33:201-235).

[0132] The PCR process is well known in the art and includes, for example, reverse transcription PCR, ligation mediated PCR, digital PCR (dPCR), or droplet digital PCR (ddPCR). For a review of PCR methods and protocols, see. e.g., Innis et al. eds., PCR Protocols. A Guide to Methods and Application, Academic Press, Inc., San Diego, Calif. 1990; U.S. Pat. No. 4,683,202 (Mullis). PCR reagents and protocols are also available from commercial vendors, such as Roche Molecular Systems. In some instances, PCR is carried out as an automated process with a thermostable enzyme. In this process, the temperature of the reaction mixture is cycled through a denaturing region, a primer annealing region, and an extension reaction region automatically. Machines specifically adapted for this purpose are commercially available.

[0133] In some embodiments, amplified sequences are also measured using invasive cleavage reactions such as the Invader(R) technology (Zou et al, 2010, Association of Clinical Chemistry (AACC) poster presentation on Jul. 28, 2010, ‘'Sensitive Quantification of Methylated Markers with a Novel Methylation Specific Technology7; and U.S. Pat. No. 7,011,944 (Prudent et al)).

[0134] Suitable next generation sequencing technologies are widely available. Examples include the 454 Life Sciences platform (Roche, Branford, Conn.) (Margulies et al. 2005 Nature, 437, 376-380); Illumina's Genome Analyzer, GoldenGate Methylation Assay, or Infinium Methylation Assays, i.e., Infinium HumanMethylation 27K BeadArray or VeraCode GoldenGate methylation array (Illumina, San Diego, Calif.; Bibkova et al, 2006, Genome Res. 16, 383-393; U.S. Pat. Nos. 6,306,597 and 7,598,035 (Macevicz); U.S. Pat. No. 7,232,656 (Balasubramanian et al.)); QX200™ Droplet Digital™ PCR System from Bio-Rad; or DNASequencing by Ligation, SOLiD System (Applied Biosystems / Life Technologies; U.S. Pat. Nos. 6,797,470, 7,083,917, 7,166,434, 7,320,865, 7,332,285, 7,364,858, and 7,429,453 (Barany et al); the Helicos True Single Molecule DNA sequencing technology (Harris et al, 2008 Science, 320, 106-109; U.S. Pat. Nos. 7,037,687 and 7,645,596 (Williams et al); 7, 169,560 (Lapidus et al); U.S. Pat. No. 7,769.400 (Harris)), the single molecule, real-time (SMRT™) technology of Pacific Biosciences, and sequencing (Soni and Meller, 2007, Clin. Chem. 53, 1996-2001); semiconductor sequencing (Ion Torrent; Personal Genome Machine); DNA nanoball sequencing; sequencing using technology from Dover Systems (Polonator), and technologies that do not require amplification or otherwise transform native DNA prior to sequencing (e.g.. Pacific Biosciences and Helicos), such as nanopore-based strategies (e.g., Oxford Nanopore, Genia Technologies, and Nabsys). These systems allow the sequencing of many nucleic acid molecules isolated from a specimen at high orders of multiplexing in a parallel fashion. Each of these platforms allows sequencing of clonally expanded or nonamplified single molecules of nucleic acid fragments. Certain platforms involve, for example, (i) sequencing by ligation of dye-modified probes (including cyclic ligation and cleavage), (ii) pyrosequencing, and (iii) single-molecule sequencing.

[0135] Pyrosequencing is a nucleic acid sequencing method based on sequencing by synthesis, which relies on detection of a pyrophosphate released on nucleotide incorporation. Generally, sequencing by synthesis involves synthesizing, one nucleotide at a time, a DNA strand complimentary to the strand whose sequence is being sought. Study nucleic acids may be immobilized to a solid support, hybridized with a sequencing primer, incubated with DNA polymerase, ATP sulfurylase, luciferase, apyrase, adenosine 5' phosphsulfate and luciferin. Nucleotide solutions are sequentially added and removed. Correct incorporation of a nucleotide releases a pyrophosphate, which interacts with ATP sulfurylase and produces ATP in the presence of adenosine 5' phosphsulfate, fueling the luciferin reaction, which produces a chemiluminescent signal allowing sequence determination. Machines for pyrosequencing and methylation specific reagents are available from Qiagen, Inc. (Valencia, Calif.). See also Tost and Gut, 2007, Nat. Prot. 2 2265-2275. An example of a system that can be used by a person of ordinary' skill based on pyrosequencing generally involves the following steps: ligating an adaptor nucleic acid to a study nucleic acid and hybridizing the study nucleic acid to a bead; amplifying a nucleotide sequence in the study nucleic acid in an emulsion; sorting beads using a picolitre multiwell solid support; and sequencing amplified nucleotide sequences bypyrosequencing methodology (e.g., Nakano et al, 2003, J. Biotech. 102, 117-124). Such a system can be used to exponentially amplify amplification products generated by a process described herein, e g., by ligating a heterologous nucleic acid to the first amplification product generated by a process described herein.

[0136] In certain embodiments, the methylation values measured for biomarkers of a biomarker panel are mathematically combined and the combined value is correlated to the underlying diagnostic question. In some instances, methylated biomarker values are combined by any appropriate state of the art mathematical method. Well-known mathematical methods for correlating a biomarker combination to a disease status employ methods like discriminant analysis (DA) (e.g., linear-, quadratic-, regularized-DA), Discriminant Functional Analysis (DFA), Kernel Methods (e.g., SVM), Multidimensional Scaling (MDS), Nonparametric Methods (e.g., k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression. CART, Random Forest Methods, Boosting / Bagging Methods), Generalized Linear Models (e.g., Logistic Regression). Principal Components based Methods (e.g., SIMCA), Generalized Additive Models, Fuzzy Logic based Methods, Neural Networks and Genetic Algorithms based Methods. The skilled artisan will have no problem in selecting an appropriate method to evaluate an epigenetic marker or biomarker combination described herein.

[0137] In one embodiment, the correlated results for each methylation panel are rated by their correlation to the likelihood of PTB and / or HDPs, such as for example, by p-value test or t- value test or F-test. Rated (best first, i.e. low p- or t-value) biomarkers are then subsequently- selected and added to the methylation panel until a certain diagnostic value is reached. Such methods include identification of methylation panels, or more broadly, genes that were differentially methylated among several classes using, for example, a random-variance t-test (Wright G. W. and Simon R, Bioinformatics 19:2448-2455,2003). Other methods include the step of specifying a significance level to be used for determining the epigenetic markers that will be included in the biomarker panel. Epigenetic markers that are differentially methylated between the classes at a univariate parametric significance level less than the specified threshold are included in the panel. It does not matter whether the specified significance level is small enough to exclude enough false discoveries. In some problems better prediction is achieved by being more liberal about the biomarker panels used as features. In some cases, the panels are biologically interpretable and clinically applicable, however, if fewer markers areincluded. Similar to cross-validation, biomarker selection is repeated for each training set created in the cross-validation process. That is for the purpose of providing an unbiased estimate of prediction error. The methylation panel for use with new patient sample data is the one resulting from application of the methylation selection and classifier of the “known’" methylation information, or control methylation panel.

[0138] Models for utilizing methylation profiles to predict the class of future samples can also be used. These models may be based on the Compound Covariate Predictor (Radmacher et al. Journal of Computational Biology 9:505-511, 2002), Diagonal Linear Discriminant Analysis (Dudoit et al. Journal of the American Statistical Association 97:77-87, 2002), Nearest Neighbor Classification (also Dudoit et al.), and Support Vector Machines with linear kernel (Ramaswamy et al. PNAS USA 98: 15149-54, 2001). The models incorporated markers that were differentially methylated at a given significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W. and Simon R. Bioinformatics 19:2448-2455, 2003). The prediction error of each model using cross validation, preferably leave-one-out cross-validation (Simon et al. Journal of the National Cancer Institute 95: 14-18, 2003 can be estimated. For each leave-one-out cross-validation training set, the entire model building process is repeated, including the epigenetic marker selection process. In some instances, it is also evaluated in whether the cross-validated error rate estimate for a model is significantly less than one would expect from random prediction. In some cases, the class labels are randomly permuted, and the entire leave-one-out cross-validation process is then repeated. The significance level is the proportion of the random permutations that gives a cross-validated error rate no greater than the cross-validated error rate obtained with the real methylation data.

[0139] Another classification method is the greedy-pairs method described by Bo and Jonassen (Genome Biology 3(4):research0017. 1-0017. 11, 2002). The greedy-pairs approach starts with the ranking of all markers based on their individual t-scores on the training set. This method attempts to select pairs of markers that work well together to discriminate against the classes.

[0140] Furthermore, a binary tree classifier for utilizing methylation profile is optionally used to predict the class of future samples. The first node of the tree incorporated a binary classifier that distinguished two subsets of the total set of classes. The individual binary classifiers are based on the “Support Vector Machines"’ incorporating markers that were differentially expressed among markers at the significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W. and Simon R. Bioinformatics 19:2448-2455, 2003).Classifiers for all possible binary partitions are evaluated and the partition selected is that for which the cross-validated prediction error is minimum. The process is then repeated successively for the two subsets of classes determined by the previous binary' split. The prediction error of the binary tree classifier can be estimated by cross validating the entire tree building process. This overall cross-validation includes re-selection of the optimal partitions at each node and re-selection of the markers used for each cross-validated training set as described by Simon et al. (Simon et al. Journal of the National Cancer Institute 95: 14-18, 2003). Several- fold cross validation in which a fraction of the samples is withheld, a binary' tree developed on the remaining samples, and then class membership is predicted for the samples withheld. This is repeated several times, each time withholding a different percentage of the samples. The samples are randomly partitioned into fractional test sets (Simon R and Lam A. BRB- ArrayTools User Guide, version 3.2. Biometric Research Branch, National Cancer Institute).

[0141] Thus, in one embodiment, the correlated results for each marker b) are rated by their correct correlation to the risk of adverse pregnancy outcome, preferably by p-value test. It is also possible to include a step in that the markers are selected d) in order of their rating.

[0142] In additional embodiments, factors such as the value, level, feature, characteristic, property, etc. of a transcription rate, mRNA level, translation rate, protein level, biological activity, cellular characteristic or property, genotype, phenotype, etc. can be utilized in addition prior to, during, or after administering a therapy to a patient to enable further analysis of the patient's likelihood of an increased risk of PTB and / or HDPs.

[0143] In some embodiments, a diagnostic test to correctly predict status is measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic (‘ ROC”) curve. In some instances, sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative. In some cases, a ROC curve provides the sensitivity7of a test as a function of 1 -specificity. The greater the area under the ROC curve, for example, the more accurate or powerful the predictive value of the test. Other useful measures of the utility of a test include positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.

[0144] In some embodiments, one or more of the biomarkers disclosed herein show a statistical difference in different samples of at least p<0.05, p<10-2, p<10-3, p<10-4 or p<10-5. Diagnostic tests that use these biomarkers may show an ROC of at least 0.6, at least about 0.7, at least about 0.8, or at least about 0.9. In some instances, the biomarkers are differentially methylated in different subjects with or without an increased risk of PTB and / or HDPs.

[0145] In some embodiments, disclosed herein is a method of determining preterm birth (PTB) or hypertensive disorders of pregnancy (HDPs) in a pregnant subject, comprising: obtaining a biological sample from a pregnant subject; stimulating the biological sample with an immunogen; assaying Toll-like receptor (TLR)-mediated functional immune responsiveness after stimulation, wherein TLR-mediated immune responsiveness comprises at least one cytokine; detecting a level of at least one cytokine, wherein the cytokine comprises pro- inflammatory cytokines or anti-inflammatory cytokines; and determining the pregnant subject is at an increased risk of PTB and / or HDP if the level of at least one cytokine is above a probability threshold level.

[0146] In some embodiments, the method further comprises determining a risk category based on the probability threshold level, wherein the probability threshold level of <0.2 indicates a low-risk category', the probability threshold level of 0.2-0.4 indicates a moderate-risk category', the probability threshold level of >0.4 indicates a high-risk category, wherein the pregnant subject in high-risk category has elevated levels of at least one cytokine as compared to a control.

[0147] As used herein, the cytokines also referred to as analytes or immune markers are detected using immunoassays. Disclosed herein are analytes that may be measured using the assay methods of the present invention including immune markers, such as cytokines, that are involved in regulation of immune response. Cytokines include the interleukins (ILs), interferons (IFNs), chemokines, tumor necrosis factors (TNFs), macrophage inflammatory' proteins (MIPs), and monocyte chemoattractant proteins (MCPs). The term cytokines, as used herein, also includes soluble cytokine receptors. Specific cytokines that may be measured in the assays of the invention include, but are not limited to, cytokines such as pro-inflammatory markers can be selected from the group comprising interleukin-6 (IL-6), tumor necrosis factoralpha (TNF-a), interleukin-1 beta (IL-1|3), interleukin-8 (IL-8), macrophage inflammatory proteins (MIPs), monocyte chemoattractant proteins (MCPs). or interleukin- 17 (IL-17). Antiinflammatory' markers can be selected from the group comprising interleukin- 10 (IL- 10), tumornecrosis factor receptor I (TNFRI), tumor necrosis factor receptor II (TNFRII), interferongamma (IFN-y), interleukin- 12p70 (IL-12p70), interleukin- 13 (IL-13), interleukin-37 (IL-37), transforming growth factors (TGFs), resolvins, and / or protectins. Immune markers reflective of Thl-type (IL-6, TNF-a. IL-ip, IFN-y, IL-12p70) vs. Th2-type (IL-10) cytokines, the Type II IFN response (IFN-y). and crosstalk between the innate and adaptive immune systems (IFN- y, IL-12p70). According to one aspect of the invention, analytes could advantageously measure in a sample obtained via a non-surgically invasive collection technique, such as in a blood, serum, plasma, fecal, or urine sample from a subject (pregnant or not pregnant).

[0148] The concentration of the analyte is measured in a specific assay setting (in this case, measurement on a SECTOR™ Imager 6000 reader) (Meso Scale Discovery, a division of Meso Scale Diagnostics, LLC, Gaithersburg, Md.) using kits for multiplexed measurements of cytokines (Meso Scale Diagnostics, LLC, Gaithersburg, Md.). The exact values of the measured levels and associated cut-off values may vary somewhat depending on the exact assay conditions and the standards used for assay calibration and natural variation between population groups, however, the general trends (e.g., relationship between the level of the immune markers and the risk of PTB and / or HDPs) can be used for different assays kits and assay instruments. One of ordinary' skill in the art can determine cut-off values applicable for a specific set of assay conditions.

[0149] One of ordinary skill in the art can select, without undue burden, appropriate cut-off values, lines, ratios, zones etc. for best meeting the needs (e.g., sensitivity and specificity) for a particular application. A variety of statistical tools, such as. for example, receiver operating characteristic (ROC) curves, are available for evaluating the effect of adjustments to cut-offs on assay performance (e.g., predicted true positive fraction, false positive fraction, true negative fraction and false negative fraction). Alternatively, statistical analysis of patient populations can allow conversion of specific analyte values into probability' that the patient has or does not have a disease. For background on the selection and analysis of populations of individuals so as to determine reference ranges see Boyd J. C. ‘"Reference Limits in the Clinical Laboratory” in Professional Practice in Clinical Chemistry: A Companion Text; D. R. Dufour Ed., 1999, Washington D.C.: American Assoc. Clin. Chem., Chapter 2, pp. 2-1 to 2-7. For background on the selection of decision limits (i.e., cut-offs) or the calculation, from test results, of disease likelihood see Boyd J. C. “Statistical Aids for Test Interpretation” inProfessional Practice in Clinical Chemistry: A Companion Text; D. R. Dufour Ed., 1999, Washington D.C.: American Assoc. Clin. Chem., Chapter 3, pp. 3-1 to 3-11.

[0150] Given the teachings of the present invention, a skilled artisan will also recognize that the choice of immune markers may transpose correlation plot axes and consequently the criteria for determining whether measured cytokine levels of a patient's samples falling above or below particular cut-off ratios, lines and / or profiles is indicative of a disease state and will be able to adjust the analysis accordingly. The cytokine levels may be measured using any of a number of techniques available to the person of ordinary skill in the art, e.g., direct physical measurements (e.g., mass spectrometry) or binding assays (e.g., immunoassays, agglutination assays, and immunochromatographic assays). The method may also comprise measuring a signal that results from a chemical reaction, e.g., a change in optical absorbance, a change in fluorescence, the generation of chemiluminescence or electrochemiluminescence, a change in reflectivity, refractive index or light scattering, the accumulation or release of detectable labels from the surface, the oxidation or reduction or redox species, an electrical current or potential, changes in magnetic fields, etc. Suitable detection techniques may detect binding events by measuring the participation of labeled binding reagents through the measurement of the labels via their photoluminescence (e.g., via measurement of fluorescence, time-resolved fluorescence, evanescent wave fluorescence, up-converting phosphors, multi-photon fluorescence, etc.), chemiluminescence, electrochemiluminescence, light scattering, optical absorbance, radioactivity, magnetic fields, enzymatic activity (e.g., by measuring enzyme activity through enzymatic reactions that cause changes in optical absorbance or fluorescence or cause the emission of chemiluminescence). Alternatively, detection techniques may be used that do not require the use of labels, e.g., techniques based on measuring mass (e.g., surface acoustic wave measurements), refractive index (e.g., surface plasmon resonance measurements), or the inherent luminescence of an analyte.

[0151] In some embodiments, quantification of cytokines may be achieved through various immunoassay techniques. These include electrochemiluminescence-based immunoassays, such as those offered by Meso Scale Diagnostics, and microfluidics-based multiplex immunoassay systems like the Ella platform from Bio-Techne. These technologies enable precise measurement of multiple cytokines simultaneously. Traditional enzyme-linked immunosorbent assay (ELISA) methods may also be employed for single or multiple immune marker detection. Moreover, point-of-care (POC) protein quantification methods, such aslateral flow assays, electrochemical immunosensors, or optical detection systems, may be utilized to enhance accessibility and applicability in diverse clinical settings. By integrating these advanced immunoassay techniques, the method allows for accurate detection of cytokine levels.

[0152] Binding assays for measuring cytokine levels may use solid phase or homogenous formats. Suitable assay methods include sandwich or competitive binding assays. Examples of sandwich immunoassays are described in U.S. Pat. No. 4,168,146 to Grubb et al. and U.S. Pat. No. 4,366,241 to Tom et al., both of which are incorporated herein by reference. Examples of competitive immunoassays include those disclosed in U.S. Pat. No. 4,235,601 to Deutsch et al., U.S. Pat. No. 4,442,204 to Liotta, and U.S. Pat. No. 5,208,535 to Buechler et al., all of which are incorporated herein by reference.

[0153] As used herein, microfluidic technologies, such as the Ella platform by Bio-Techne, represent a significant advancement in cytokine detection. These systems integrate immunoassay chemistry with microfluidic channels to automate sample preparation, reaction, and analysis. Using microfluidic cartridges preloaded with antibodies specific to various cytokines, this technology can simultaneously quantify multiple cytokines in a single run. The small volume requirements (often just a few microliters) and rapid processing time make these assays highly efficient.

[0154] Also used herein, is lateral flow assay (LFA) for the detection of cytokines which utilize a test strip where antibodies specific to the target cytokine are immobilized on a nitrocellulose membrane. As the sample flows laterally across the strip, the target cytokine binds to the labeled detection antibodies, forming a visible signal, often as a colored line. LFAs are particularly valuable in settings requiring quick, qualitative or semi-quantitative results, though their sensitivity and dynamic range may be lower than other immunoassay methods. In some examples, electrochemical immunosensors are used to detect cytokines by converting a biological binding event into an electrical signal. These devices typically consist of an electrode functionalized with antibodies specific to the cytokine of interest. Upon cytokine binding, changes in the electrochemical properties at the electrode surface are detected and quantified. This method offers high sensitivity, portability, and real-time detection capabilities, making it suitable for POC applications. Advances in nanomaterials and electrode design have further enhanced the performance of electrochemical immunosensors. In other examples, optical immunoassays are used to detect changes in light properties, such as fluorescence, absorbance,or surface plasmon resonance (SPR), upon cytokine-antibody interactions. For example, fluorescent immunoassays utilize labeled antibodies that emit light when excited by a specific wavelength, while SPR-based assays measure refractive index changes at a sensor surface upon cytokine binding.

[0155] Multiple cytokines may be measured using a multiplexed assay format, e.g., multiplexing through the use of binding reagent arrays, multiplexing using spectral discrimination of labels, multiplexing by flow cytometric analysis of binding assays carried out on particles (e g., using the Luminex system). Suitable multiplexing methods include array based binding assays using patterned arrays of immobilized antibodies directed against the cytokines of interest. Various approaches for conducting multiplexed assays have been described. For example, multiplexed testing is described in U.S. patent application Ser. Nos. 10 / 185,274 and 10 / 185,363, both filed on Jun. 28, 2002, entitled “Assay Plates, Reader Systems and Methods For Luminescence Test Measurements,” published as U.S. Pat. Publ. No. 20040022677 and US20050052646, respectively. U.S. patent application Ser. No. 10 / 238,960, filed Sep. 10, 2002, entitled “Methods, Reagents, Kits and Apparatus for Protein Function,” published as U.S. Pat. Publ. No. 20030207290, U.S. patent application Ser. No. 10 / 238,391, filed Sep. 10, 2002, entitled “Methods and apparatus for conducting multiple measurements on a sample”; published as U.S. Pat. Publ. No. 20030113713, U.S. patent application Ser. No. 10 / 980,198, filed onNov. 3, 2004, entitled “Modular Assay Plates, Reader System and Methods For Test Measurements,” published as U.S. Pat. Publ. No. 20050142033; and U.S. patent application Ser. No. 10 / 744,726, filed on Dec. 23, 2003, entitled “Assay Cartridges and Methods of Using Same,” published as U.S. Pat. Publ. No. 20040189311. each of which is incorporated by this reference. One approach to multiplexing binding assays involves the use of patterned arrays of binding reagents (see, e.g., U.S. Pat. Nos. 5,807,522 and 6,110,426, both entitled “Methods for Fabricating Microarrays of Biological Samples” issued Sep. 15, 1998 and Aug. 29, 2000 respectively, Delehanty J B, Printing functional protein microarrays using piezoelectric capillaries, Methods Mol Biol. (2004) 278: 135-44; Lue R Y, Chen G Y, Zhu Q, Lesaicherre M L, Yao S Q, Site-specific immobilization of biotinylated proteins for protein microarray analysis, Methods Mol Biol. (2004) 278:85-100; Lovett, Toxicogenomics: Toxicologists Brace for Genomics Revolution, Science (2000) 289: 536-537; Berns A., Cancer: Gene expression in diagnosis. Nature (2000) 403. 491-492: Walt, Molecular Biology: Bead-based Fiber-Optic Arrays, Science (2000) 287: 451-452 for more details).Another approach involves the use of binding reagents coated on beads that can be individually identified and interrogated. International Patent publication WO9926067A1 (Watkins et al.) describes the use of magnetic particles that var7in size to assay multiple analytes; particles belonging to different distinct size ranges are used to assay different analytes. The particles are designed to be distinguished and individually interrogated by flow cytometry. Vignali has described a multiplex binding assay in which 64 different bead sets of microparticles are employed, each having a uniform and distinct proportion of two dyes (Vignali, D. A. A., “Multiplexed Particle-Based Flow Cytometric Assays,” J. Immunol. Meth. (2000) 243:243- 255). A similar approach involving a set of 15 different beads of differing size and fluorescence has been disclosed as useful for simultaneous typing of multiple pneumococcal serotypes (Park, M. K. et al., “A Latex Bead-Based Flow Cytometric Immunoassay Capable Of Simultaneous Typing Of Multiple Pneumococcal Serotypes (Multibead Assay),” Clin Diagn Lab Immunol. (2000) 7:486-9). Bishop. J. E. et al. have described a multiplex sandwich assay for simultaneous quantification of six human cytokines (Bishop, J. E. et al., “Simultaneous Quantification of Six Human Cytokines in a Single Sample Using Microparticle-based Flow Cytometric Technology,” Clin Chem. (1999) 45: 1693-1694).

[0156] Advantageously, in certain embodiments, tests may be conducted on a single sample including, but not limited to, blood, serum, plasma, tissue, biopsies, tissue extracts, cells, cell extracts, cell culture supernatants, and lymphatic fluids. Particularly advantageous are blood, blood serum, blood plasma due to the easy and non-surgically invasive collection techniques.

[0157] A diagnostic test may also be conducted in a single assay chamber, such as a single well of an assay plate or an assay chamber that is an assay chamber of a cartridge. The assay modules (for example assay plates or cartridges, or multi-well assay plates), methods and apparatuses for conducting assay measurements suitable for the present invention are described, for example, in U.S. patent application Ser. Nos. 10 / 185,274 and 10 / 185,363, both filed on Jun. 28, 2002, entitled “Assay Plates, Reader Systems and Methods For Luminescence Test Measurements,” published as U.S. Pat. Publ. No. 20040022677 and US20050052646, respectively, U.S. patent application Ser. No. 10 / 980,198, filed on Nov. 3, 2004, entitled “Modular Assay Plates, Reader System and Methods For Test Measurements,” published as U.S. Pat. Publ. No. 20050142033, and U.S. patent application Ser. No. 10 / 744,726, filed on Dec. 23, 2003, entitled “Assay Cartridges and Methods of Using Same,” published as U.S. Pat. Publ. No. 2004018931 1, each of which is incorporated by this reference. Assay plates and platereaders are now commercially available (MULTI-SPOT® and MULTI-ARRAY™ plates and SECTOR™ instruments, Meso Scale Discover}', a division of Meso Scale Diagnostics, LLC, Gaithersburg, Md.).

[0158] As disclosed herein, electrochemiluminescence measurements were carried out using multi-well plates having integrated carbon ink electrodes (MULTI-SPOT® plates from Meso Scale Discovery, a division of Meso Scale Diagnostics, LLC.). A dielectric layer patterned over the working electrode in each well exposed ten regions or “spots” on the working electrode. The basic detection technology is described in U.S. patent application Ser. Nos. 10 / 185,274 and 10 / 185,363, both filed on Jun. 28, 2002, entitled “Assay Plates, Reader Systems and Methods for Luminescence Test Measurements,” published as U.S. Pat. Publ. No. 20040022677 and US20050052646, respectively. Kits for multiplexed measurements of cytokines using this technology are available from Meso Scale Diagnostics, LLC, Gaithersburg, Md. The kits include plates with an array of capture antibodies on the “spots” of each well. The measurements described herein used measured a cytokine panel. Cytokines include the interleukins (ILs), interferons (IFNs), chemokines, tumor necrosis factors (TNFs), macrophage inflammatory proteins (MIPs), and monocyte chemoattractant proteins (MCPs). The term cytokines, as used herein, also includes soluble cytokine receptors. Specific cytokines that may be measured in the assays of the invention include, but are not limited to, cytokines such as pro-inflammatory markers can be selected from the group comprising interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), interleukin-1 beta (IL-ip), interleukin-8 (IL-8), macrophage inflammatory proteins (MIPs), monocyte chemoattractant proteins (MCPs), or interleukin- 17 (IL-17). Anti-inflammatory markers can be selected from the group comprising interleukin- 10 (IL- 10), tumor necrosis factor receptor I (TNFRI), tumor necrosis factor receptor II (TNFRII), interferon-gamma (IFN-y), interleukin- 12p70 (IL-12p70), interleukin- 13 (IL-13), interleukin-37 (IL-37), transforming growth factors (TGFs), resolvins, and / or protectins. Immune markers reflective of Thl- (IL-6. TNF-a, IL-ip, IFN-y, IL-12p70) vs. Th2-type (IL- 10) cytokines, the Type II IFN response (IFN-y), and crosstalk between the innate and adaptive immune systems (IFN-y, IL-12p70).Kits

[0159] In some embodiments, provided herein include kits for detecting and / or characterizing the methylation profile of a biomarker described herein. In some instances, the kit comprises a plurality of primers or probes to detect or measure the methylation status / levels of one or moresamples. Such kits comprise, in some instances, at least one polynucleotide that hybridizes to at least one of the methylation marker sequences described herein and at least one reagent for detection of gene methylation. Reagents for detection of methylation include, e.g., sodium bisulfate, polynucleotides designed to hybridize to sequence that is the product of a marker sequence if the marker sequence is not methylated (e.g., containing at least one C-U conversion), and / or a methylation-sensitive or methylation-dependent restriction enzyme. In some cases, the kits provide solid support in the form of an assay apparatus that is adapted to use in the assay. In some instances, the kits further comprise detectable labels, optionally linked to a polynucleotide, e.g.. a probe, in the kit.

[0160] Also disclosed herein are kits comprising markers for determining status of immune markers in a subject, wherein the markers are toll-like receptor (TLR)-mediated. The kit can be used to determine status of TLR1-TLR10 regulation and / or responsiveness. The immune marker(s) are pro-inflammatory and / or anti-inflammatory, as described herein. The kit can comprise one or more stimulants of a toll-like receptor. Examples include, but are not limited to, lipopolysaccharide, gardiquimod, Poly(LC), and / or zy mosan. The kit can also include an incubator, as shown in Figure 7. A multiplex immunoassay can also be included.

[0161] In some embodiments, the kits comprise one or more (e.g., 1, 2, 3, 4, or more) different polynucleotides (e.g., primers and / or probes) capable of specifically amplifying at least a portion of a DNA region of a biomarker described herein. Optionally, one or more detectably labeled polypeptides capable of hybridizing to the amplified portion are also included in the kit. In some embodiments, the kits comprise sufficient primers to amplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNA regions or portions thereof, and optionally include detectably labeled polynucleotides capable of hybridizing to each amplified DNA region or portion thereof. The kits further can comprise a methylation-dependent or methylation sensitive restriction enzyme and / or sodium bisulfite.

[0162] In some embodiments, the kits comprise sodium bisulfite, primers and adapters (e.g., oligonucleotides that can be ligated or otherwise linked to genomic fragments) for whole genome amplification, and polynucleotides (e.g., detectably labeled polynucleotides) to quantify the presence of the converted methylated and or the converted unmethylated sequence of at least one cytosine from a DNA region of an epigenetic marker described herein.

[0163] In some embodiments, the kits comprise methylation sensing restriction enzymes (e.g., a methylation-dependent restriction enzyme and / or a methylation-sensitive restrictionenzyme), primers and adapters for whole genome amplification, and polynucleotides to quantify the number of copies of at least a portion of a DNA region of an epigenetic marker described herein.

[0164] In some embodiments, the kits comprise a methylation binding moiety and one or more polynucleotides to quantify the number of copies of at least a portion of a DNA region of a marker described herein. A methylation binding moiety refers to a molecule (e.g., a polypeptide) that specifically binds to methyl-cytosine.

[0165] Examples include restriction enzy mes or fragments thereof that lack DNA cutting activity but retain the ability to bind methylated DNA, antibodies that specifically bind to methylated DNA, etc.).

[0166] In some embodiments, the kit includes a packaging material. As used herein, the term “packaging material'’ can refer to a physical structure housing the components of the kit. In some instances, the packaging material maintains sterility of the kit components, and is made of material commonly used for such purposes (e.g., paper, corrugated fiber, glass, plastic, foil, ampules, etc.). Other materials useful in the performance of the assays are included in the kits, including test tubes, transfer pipettes, and the like. In some cases, the kits also include written instructions for the use of one or more of these reagents in any of the assays described herein.

[0167] In some embodiments, kits also include a buffering agent, a preservative, or a protein / nucleic acid stabilizing agent. In some cases, kits also include other components of a reaction mixture as described herein. For example, kits include one or more aliquots of thermostable DNA polymerase as described herein, and / or one or more aliquots of dNTPs. In some cases, kits also include control samples of known amounts of templated DNA molecules harboring the individual alleles of a locus. In some embodiments, the kit includes a negative control sample, e.g., a sample that does not contain DNA molecules harboring the individual alleles of a locus. In some embodiments, the kit includes a positive control sample, e.g., a sample containing known amounts of one or more of the individual alleles of a locus.EXAMPLESExample 1: Maternal innate immune responsiveness and future birth timing: Findings from a low-risk, Black American cohort

[0168] Using maternal samples collected at 28-32 weeks gestation, associations among traditional maternal immune parameters, markers of TLR-mediated maternal immuneresponsiveness, and future birth timing were examined. Individuals at low risk for complications of pregnancy were the focus, maximizing the ability to witness spontaneously initiated birth. Black Americans, who bear a disproportionate burden of risk for sPTB for unknown reasons, were the focus. It was hypothesized that markers of TLR-mediated immune responsiveness, but not traditional immune parameters, can predict future birth timing, including after adjustment for confounders. It was hypothesized that this association would persist when early, medically initiated births were excluded, showing that markers of immune responsiveness could be targeted in future studies of sPTB risk prediction, including among Black American populations.Material and MethodsStudy design, setting, and participants

[0169] To address the research objectives, a prospective cohort study was conducted with data collection spanning 2013-2015. The convenience sample of pregnant individuals was recruited from a populous Midwestern city in the US using community advertisements and direct recruitment from two prenatal care locations. Inclusion criteria included singleton pregnancy and ultrasound-confirmed pregnancy dating, which was required to occur before 16 weeks gestation (‘Committee Opinion No 700: Methods for Estimating the Due Date’, 2017). Eligible individuals also self-identified as non-Hispanic and Black and were US-bom and raised. Exclusion criteria included maternal age <18 or >34 (at conception), maternal body mass index < 18.5 or > 39.9 (at pre-pregnancy), self-report of tobacco or marijuana use after the first trimester, self-report of alcohol or drug use after pregnancy discovery, fetal anomaly (e.g., anencephaly, gastroschisis), major complication of pregnancy diagnosed before enrollment [e.g., hypertensive disorder of pregnancy (HDP), gestational diabetes (GDM)], chronic illness (e.g., heart disease, Type I or II diabetes mellitus), and regular use of an immunomodulatory medication (e.g., oral steroids).Data collection procedures

[0170] Participants were enrolled and data collected at a single prenatal study visit, standardized to occur at 30 + 2 weeks of pregnancy (i.e., the early third trimester). Participants were asked to report illness or fever, use of antibiotics, antivirals, or steroids, and receipt of vaccination within seven days of a planned visit, to allow for rescheduling as needed.Participant temperatures were also recorded at the study visit, with no participants exhibiting a fever. During the visit, participants also completed interviews and questionnaires, and provided ablood sample. Following the birth of the baby, medical records of prenatal, labor and delivery, and newborn care were reviewed, and clinical data was manually abstracted.Variables, data sources, and measurement

[0171] Maternal complete blood cell counts with electronic differential. K2EDTA- anticoagulated maternal whole blood was delivered to the clinical laboratory at the university medical center. Complete blood cell counts with electronic differential were completed using volume, conductivity, and scatter technology7(Beckman Coulter, Brea, CA). Absolute counts of total and individual white blood cells were used for analyses.

[0172] Maternal circulating pro-inflammatory cytokine levels. Heparinized maternal whole blood was placed on ice at sampling, followed by refrigerated centrifugation, plasma aspiration, sample aliquoting, and storage at -80°C. Upon first thaw7, four pro-inflammatory cytokines [interleukin (IL)-6, IL-ip, tumor necrosis factor (TNF)-a, IL-8] were assayed in duplicate by multi-spot electrochemiluminescence per manufacturer recommendations (Meso Scale Discovery, Inc., Gaithersburg, MD). Intra- and inter-assay coefficients of variation for the assayed samples were as follows: IL-6 (7.1%, 4.0%), IL- 1(3 (15.5%, 2.3%), TNF-a (3.9% 7.2%), IL-8 (4.0%, 3.6%). Plasma pro-inflammatory cytokine levels were used for analyses.

[0173] Maternal immune responsiveness. Heparinized maternal whole blood was placed on ice at sampling. Under sterile conditions, in hood, maternal whole blood was added to two solutions: 1) a lipopolysaccharide solution to produce a stimulated condition, and 2) sterile media to produce a control condition. Whole blood cultures were incubated, followed by refrigerated centrifugation, supernatant aspiration, sample aliquoting, and storage at -80°C. Upon first thaw, four pro-inflammatory cytokines (IL-6. IL-1 [3, TNF-a. IL-8) and four antiinflammatory cytokines [IL- 10, IL-1 receptor antagonist (Ra), TNF receptor (TNFR) I, and TNFRII] were assayed in duplicate by multi-spot electrochemiluminescence per manufacturer recommendations (Meso Scale Discovery7, Inc., Gaithersburg, MD). Intra- and inter-assay coefficients of variation for the assayed samples were as follows: IL-6 (10.8%. 12.2%), IL-ip (6.1%, 4.5%), TNF-a (9.1%, 13.6%), IL-8 (10.8%, 11.1%), IL-10 (26.1%, 7.9%), IL-IRa (9.4%, 17.2%), TNFRI (6.0%, 12.1%), and TNFRII (4.1%, 6.4%), respectively. For each cytokine, maternal immune responsiveness w as defined as pro- and anti-inflammatory7cytokine production under the stimulated condition minus production under the control condition.

[0174] Timing and circumstances of birth. Each participant was followed from the time of enrollment at 28-32 weeks gestation until after the birth of the baby. At this time, prenatal, labor, delivery, and inpatient postpartum medical records were retrieved and reviewed in detail. Data was manually abstracted by trained research personnel, including obstetric estimate of due date, actual date of birth, chief complaint at presentation to labor and delivery, medical interventions during labor, and mode of birth. Birth timing was calculated. Births were also classified as medically initiated (i.e., by induction of labor or pre-labor cesarean section) or spontaneously initiated (i.e., by regular uterine contractions, rupture of membranes, or painless cervical dilation).

[0175] Statistical analysis. First, data were examined in detail. Four enrolled participants were excluded from analyses due to missing blood samples (n=3) or loss to follow up (n=l), leaving an analytical sample of 92. Also of note, birth timing showed significant negative skew. Considering the implications of this distribution for parametric assumption violation, birth timing was transformed [i.e.. A / (-(days gestation) + 288 + 1), with 288 serving as the maximum recorded value and one serving as a constant]. In the text, the direction of reported results is reversed to account for the variable’s reflection and ease interpretation of findings. Participant characteristics were also examined and described according to mean + standard deviation or count (frequency), as appropriate. In preparation for planned sensitivity analyses, participant characteristics were also summarized and compared among participants with (« = 8) versus without (n = 84) a medically initiated birth prior to full term, using independent sample t tests or fisher exact tests, as appropriate.

[0176] Next, using OLS regression, associations were examined among 1) traditional maternal immune parameters (i.e., white blood cell counts, plasma pro-inflammatory cytokine levels) and future birth timing, and 2) maternal immune responsiveness (i.e., pro- and antiinflammatory stimulated cytokine production) and future birth timing. The process of Hosmer, Lemeshow, and Sturdivant were followed for variable selection, incorporating consideration of the strong potential for nonlinear associations. A final traditional model and a final model of immune responsiveness was produced, comparing fit of competing models using likelihood ratio (i.e., Wilks) tests. Each model was presented as unadjusted and adjusted for maternal sociodemographic (i.e., age, relationship status, educational attainment), health behaviors (i.e., tobacco use, alcohol intake, pre-pregnancy body mass index), clinical history (i.e., gravidity,parity), and prior pregnancy complications (i.e., history of PTB, history of HDP, history of GDM).

[0177] Next, whether immune perturbations co-occur with identified or even unidentified complications of the current pregnancy was studied, it’s possible that associations among maternal immune parameters and future birth timing are driven by early births that were medically initiated due to provider concern for the mother and / or fetus. Therefore, a set of sensitivity analyses was completed, repeating the above steps while excluding these cases (n = 8). For the primary and sensitivity analyses, post-estimation diagnostics were examined. While there was one outlying value for TNF-a production and one outlying value for IL-8 production [i.e., > (M + (3*SD))], these observations did not exert undue influence on unadjusted or fully adjusted model estimates (e.g., Cook’s distance < 0.047). Thus, the data points were retained. All analyses were performed using STATA 17.0 (College Station, TX), with a set at 0.05.ResultsParticipant characteristics

[0178] Table 1 presents participant characteristics among the full analytical sample (n = 92), those with a medically initiated birth prior to full term (n = 8), and those without a medically initiated birth prior to full term (n = 84). In comparing these two subgroups, those with versus without medically initiated birth prior to full term were marginally less likely to report tobacco use in the year prior to study enrollment (p = 0.070). Further, while pregnancy complications were relatively rare (9 cases of gestational hypertension, 3 cases of preeclampsia, 8 cases of poly / oligohy dramnios. 3 cases of chorioamnionitis, 0 cases of GDM, placenta previa, placenta accreta, or placental abruption), those with versus without medically initiated birth prior to full term w ere significantly more likely to have an HDP (p = 0.001).Descriptive statistics

[0179] Among the full analytical sample (n = 92), maternal blood was collected at a mean 30 weeks 3.2 days gestation (SD 10.3 days). Participants gave birth at an average of 39 weeks 0.6 days (SD 9.7 days). The earliest birth occurred at 34 weeks 1 day and the latest birth occurred at 41 weeks 1 day. Seven (7.6%) participants gave birth preterm. Two (2.1%) of these PTBs were medically initiated and five (5.4%) were spontaneously initiated. Overall, 54 (58.7%)participants experienced the spontaneous onset of labor while 38 (41.3%) were induced (n = 23) or underwent cesarean section in the absence of spontaneous labor (n = 15).

[0180] Mean white blood cell counts were 9.58 (SD 2.34) cells / pL. Mean levels of plasma IL- 6, IL-ip, TNF-a, and IL-8 were 1.08 (SD 1.83), 0.09 (SD 0.06), 1.65 (SD 0.48), and 3.48 (SD 2.20) pg / mL. respectively. Mean levels of IL-6, IL-1[3. TNF-a, IL-8, IL-10. IL-IRa, TNFRI, and TNFRII production, using our whole blood stimulation protocol, were 786.96 (SD 377.09), 41.25 (SD 30.85), 462.36 (SD 249.84), 856.77 (SD 503.08), 0.22 (SD 0.25), 4818.89 (SD 1431.4), 69.12 (SD 73.67), and 59.27 (SD 67.70) pg / mL, respectively.Traditional maternal immune parameters and future birth timing

[0181] Maternal white blood cell counts and plasma levels of four pro-inflammatory cytokines were examined for potential associations with future birth timing using OLS regression. White blood cell counts showed no predictive power in bivariate analyses and were not considered further (p values > 0.272). Plasma IL-6, IL- 10, and IL-8 levels showed potential in bivariate analyses and were considered further. However, Wilks tests indicated no added benefit of including IL- 1 p or IL-8 and no evidence of polynomial functions, resulting in a final model in which only higher levels of plasma IL-6 were associated with earlier birth timing, but not significantly (beta = -0.116, t (90) = -1.10, / ? = 0.272, R2= 0.013, adj. R2= 0.002).

[0182] The association between plasma IL-6 levels and future birth timing was then examined, sequentially adjusting for sociodemographic, health behaviors, clinical history, and prior pregnancy complications. Results were similar, with plasma IL-6 levels showing a negative but non-significant association with birth timing in the fully adjusted model (beta = -0.112, t (79) = -1.03, p = 0.308, R2= 0.088, adj. R2= -0.051).Maternal immune responsiveness and future birth timing

[0183] Next, four pro-inflammatory and four anti-inflammatory markers of maternal immune responsiveness were examined for potential associations with future birth timing using OLS regression. Based on bivariate results. IL-6. IL-1 , TNF-a. IL-8. IL-10, IL-IRa. and TNFRII production were considered further. However, the model could be successfully reduced, with TNF-a, IL-8, and TNFRII retained. Together, these markers explained 18.9% (adj. R2= 0. 122) of the variance in future birth timing, with evidence of a cubic association for TNF-a (TNF- aA3: beta = 2.152, t (84) = 2.36, p = 0.021) and quadratic associations for IL-8 and TNFRII (IL-8A2: beta = 1.052, t (84) = 3.06, p = 0.003; TNFRII 2: beta = 0.316, t (84) = 2.45, p =0.016). Patterns of associations are presented in FIGS. 1 A-1F with expectant mothers with the earliest births showing low and high TNF-a production, moderate and high IL-8 production, and low and high TNFRII production.

[0184] Shown in Table 2. associations among maternal immune responsiveness and future birth timing remained similar while sequentially controlling for sociodemographic, health behaviors, and clinical history (p values < 0.037). When prior pregnancy complications were added to the model, associations among TNF-a, TNFRII, and birth timing were slightly attenuated.Sensitivity analyses excluding early, medically initiated births

[0185] Since perturbations in cellular innate immunity may co-occur with complications of pregnancy, and complications of pregnancy increase risk for early, medically initiated birth, a set of sensitivity analyses were performed, excluding early, medically initiated births (n = 8). Again, TNF-a, IL-8, and TNFRII production was associated with future birth timing, with evidence of cubic (TNF-aA3: beta = 2.394, t (76) = 2.43, p = 0.017) and quadratic patterns to the data (IL-8A2: beta = 0.967. t (76) = 2.62.p = 0.011; TNFRIIA2: beta = 0.345, t (76) = 2.43, p = 0.018). In the sensitivity analysis, which had a sample size of 84 individuals, markers of TLR4-mediated maternal immune responsiveness explained 18.4% of the variance in future birth timing (adj. R2= 0.109). Also of note, associations among markers of TLR4-mediated maternal immune responsiveness and future birth timing persisted in the fully adjusted model (p values < 0.046).Tables from Example 1:Table 1. Participant CharacteristicsFull Sample Medically initiated birth < 39 weeksCharacteristic (n=92) YES (n=8) NO (n=84)>Bachelor degreeYes 24(26.1%) 2(25.0%) 22(26.2%)Note: Presented as mean + SD or n (%).Table 2. Associations among maternal immune responsiveness and future birth timing in a Black American cohort (n=92)References for Example 1:• Amabebe, E. et al. (2019) ‘Infection / inflammation-associated preterm delivery7within 14 days of presentation with symptoms of preterm labour: A multivariate predictive model’. PloS One, 14(9), p. e0222455. Available at: https: / / doi.org / 10.1371 / joumal.pone.0222455.• Barros, F.C. et al. (2015) ‘The distribution of clinical phenotypes of preterm birth syndrome: implications for prevention’, JAMA pediatrics, 169(3), pp. 220-229. Available at: https: / / doi.org / 10.1001 / jamapediatrics.2014.3040.• Brien, M.-E. et al. (2019) ‘Alarmins at the matemal-fetal interface: involvement of inflammation in placental dysfunction and pregnancy complications 1’, Canadian Journal of Physiology and Pharmacology, 97(3), pp. 206-212. Available at: https: / / doi.org / 10. ! 139 / cjpp-2018-0363.• Chawanpaiboon, S. et al. (2019) ‘Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis’, The Lancet. Global Health, 7(1), pp. e37-e46. Available at: https: / / doi.org / 10. 1016 / S2214-109X(18)30451-0.• Cobo, T. et al. (2017) ‘Impact of microbial invasion of amniotic cavity and the ty pe of microorganisms on short-term neonatal outcome in women with preterm labor and intact membranes’, Acta Obstetricia Et Gynecologica Scandinavica, 96(5), pp. 570-579. Available at: https: / / doi.org / 10.! 111 / aogs. 13095.• Combs, C.A. et al. (2014) ‘Amniotic fluid infection, inflammation, and colonization in preterm labor with intact membranes', American Journal of Obstetrics and Gynecology. 210(2), p. 125. el-125. el 5. Available at: https: / / doi.Org / 10.1016 / j.ajog.2013. l l .032.• ‘Committee Opinion No 700: Methods for Estimating the Due Date’ (2017)Obstetrics and Gynecology, 129(5), pp. el50-el54. Available at: https : / / doi. org / 10.1097 / AOG.0000000000002046.• Eidem, H.R. et al. (2015) ‘Gestational tissue transcriptomics in term and preterm human pregnancies: a systematic review and meta-analysis’, BMC medical genomics, 8, p. 27. Available at: https: / / doi.org / 10.1186 / sl2920-015-0099-8.• Firmal, P., Shah, V.K. and Chattopadhyay, S. (2020) ‘Insight Into TLR4-Mediated Immunomodulation in Normal Pregnancy and Related Disorders’, Frontiers in Immunology, 11, p. 807. Available at: https: / / doi.org / 10.3389 / fimmu.2020.00807.• Galazis, N. et al. (2013) ‘Proteomic biomarkers of preterm birth risk in women with polycystic ovary syndrome (PCOS): a systematic review and biomarker database integration’, PloS One, 8(1), p. e53801. Available at: https: / / doi.org / 10.1371 / joumal.pone.0053801.• Heron, M. (2021) ‘Deaths: Leading Causes for 2018’. National Vital Statistics Reports: From the Centers for Disease Control arid Prevention, National Center for Health Statistics. National Vital Statistics System. 70(4), pp. 1-115.• Huang, G. et al. (2022) ‘Emerging role of toll-like receptors signaling and its regulators in preterm birth: a narrative review’, Archives of Gynecology and Obstetrics [Preprint]. Available at: https: / / doi.org / 10.1007 / s00404-022-06701-2.• Kim. S. A. et al. (2020) ‘Inflammatory Proteins in the Amniotic Fluid, Plasma, and Cervicovaginal Fluid for the Prediction of Intra-amniotic Infection / Inflammation and Imminent Preterm Birth in Preterm Labor’, American Journal of Perinatology [Preprint]. Available at: https: / / doi.org / 10.1055 / s-0040-1718575.• Liu, L. et al. (2016) ‘Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the Sustainable Development Goals’. Lancet (London. England), 388(10063). pp. 3027-3035. Available at: https: / / doi.org / 10. 1016 / S0140-6736(16)31593-8.• Manuck, T.A. et al. (2015) ‘The phenotype of spontaneous preterm birth: application of a clinical phenotyping tool’, American Journal of Obstetrics and Gynecology, 212(4), p. 487. el-487. el l. Available at: https: / / doi.Org / 10.1016 / j.ajog.2015.02.010.• McLaurin, K. et al. (2019) ‘Characteristics and health care utilization of otherwise healthy commercially and Medicaid-insured preterm and full-term infants in the US’, Pediatric health, medicine and therapeutics, 10. Available at: https : / / doi. org / 10.2147 / PHMT. S 182296.• Mor, G , Aldo, P. and Alvero, A.B. (2017) ‘The unique immunological and microbial aspects of pregnancy’, Nature Reviews. Immunology, 17(8), pp. 469-482. Available at: https: / / doi.org / 10.1038 / nri.2017.64.• Osterman, M.J.K. et al. (2023) ‘Births: Final Data for 2021’, National Vital Statistics Reports: From the Centers for Disease Control and Prevention. National Center for Health Statistics, National Vital Statistics System, 72(1), pp. 1-53.• Ragsdale, H.B. et al. (2019) ‘Regulation of inflammation during gestation and birth outcomes: Inflammatory cytokine balance predicts birth weight and length’, American Journal of Human Biology: The Official Journal of the Human Biology Council, 31(3), p. e23245. Available at: https: / / doi.org / 10.1002 / ajhb.23245.• Robertson, S. A. et al. (2020) ‘Targeting Toll-like receptor-4 to tackle preterm birth and fetal inflammatory injury’, Clinical & Translational Immunology, 9(4), p. el 121. Available at: https: / / doi.org / 10.1002 / cti2.1121.• Romero, R. et al. (2006) ‘Inflammation in preterm and term labour and delivery’, Seminars in Fetal & Neonatal Medicine, 11(5), pp. 317-326. Available at: https: / / doi.Org / 10.1016 / j.siny.2006.05.001.• Romero, R. et al. (2007) ‘The role of inflammation and infection in preterm birth’. Seminars in Reproductive Medicine, 25(1), pp. 21-39. Available at: https: / / doi.org / 10.1055 / s- 2006-956773.• Ross, K.M. et al. (2019) ‘Pro-inflammatory immune cell gene expression during the third trimester of pregnancy is associated with shorter gestational length and lower birthweight’, American Journal of Reproductive Immunology (New York, N. Y.: 1989), 82(6), p. e!3190. Available at: https: / / doi.org / 10. l l l l / aji.13190.• Strauss, J.F. et al. (2018) ‘Spontaneous preterm birth: advances toward the discovery of genetic predisposition’. American Journal of Obstetrics and Gynecology:, 218(3), pp. 294- 314.e2. Available at: https: / / doi.Org / 10.1016 / j.ajog.2017.12.009.• Tency, I. (2014) ‘Inflammatory response in maternal serum during preterm labour’, Facts, Views & Vision in ObGyn, 6(1), pp. 19-30.• Triggs, T., Kumar, S. and Mitchell, M. (2020) ‘Experimental drugs for the inhibition of preterm labor', Expert Opinion on Investigational Drugs, 29(5), pp. 507-523. Available at: https: / / doi.org / 10.1080 / 13543784.2020.1752661.• Villar, J. et al. (2021) ‘Maternal and Neonatal Morbidity' and Mortality Among Pregnant Women With and Without COVID-19 Infection: The INTERCOVID Multinational Cohort Study’, JAMA pediatrics, 175(8), pp. 817-826. Available at: https: / / doi.org / 10.1001 / jamapediatrics.2021.1050.• Vora, B. et al. (2018) ‘Meta- Analy sis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth’, Frontiers in Immunology, 9, p. 993. Available at: https: / / doi.org / 10.3389 / fimmu.2018.00993.Example 2: Functional Immune Responsiveness to Toll-Like Receptor (TLR) 4Stimulation in Early Pregnancy

[0186] There is broad consensus that term and preterm labor processes involve a powerful pro- inflammatory cascade. Indeed, localized and systemic immune markers rise considerably during labor and inflammation is the most common gross pathological finding in sPTB. Yet, the field has tirelessly pursued passively measured immune stimulants (i.e., pathogens, endogenous danger signals) and immune markers in future sPTB risk prediction, without success. Such data prompted critical consideration of the functional immune adaptations that are also witnessed during pregnancy and attempt to understand their physiological intent and potential to introduce risk. Specifically, pregnancy is now recognized as an immunotol erant state involving a shift toward mucosal immunity and away from cellular innate immunity, with leukocytes responding considerably more vigorously to innate immune stimulation during labor than during quiescent pregnancy. The widely held belief is that such adaptations sen e to protect against fetal allograft rejection. Also, it appears that cellular innate immunity7may also gradually reapproximate a responsive, pro-inflammatory phenotype in preparation for labor. And, therefore, premature re-approximation of this phenotype may serve as a key driver of sPTB. Supported by literature and data, it appears that aberrations in functional immune responsiveness not only drive a significant portion of sPTBs but can also be clinically identified in early pregnancy using ex vivo methods and targeted for the clinical prediction and prevention of future sPTB.

[0187] Toll-like Receptors. Pathogen-associated molecular patterns (PAMPs) and damage- associated molecular patterns (DAMPs) are recognized by evolutionarily conserved receptors called toll-like receptors (TLRs). TLRs are expressed by immune, epithelial, and endothelial cells as well as fibroblasts and initiate a signaling cascade resulting in an innate immune response upon stimulation. Gram-negative bacteria (e.g.. Fusobacterium and E. coli species. N. gonorrhoeae, C. trachomatis, H. influenzae, K. pneumoniae)' , viral proteins (e.g., SARS- CoV-2 spike, respiratory syncytial virus R, hepatoviral C NS3 proteins), and endogenous ligands (e.g., heat shock protein 70 and 90, high mobility group box 1) stimulate extracellular TLR4. Gram-positive bacteria (e.g., Ureaplasma species, G. vaginalis, S. aureus, S. pneumoniae, S. pyogenes, M. tuberculosis, C. perfringes), viral lipoproteins (e.g., SARS-CoV- 2, adenovirus, enterovirus, and cytomegalovirus envelopes), fungi (e.g., Candida species), and endogenous ligands (e g., heat shock protein 60) stimulate extracellular TLR2. Viral double-stranded RNA (e.g., rotavirus, Zika virus) stimulates intracellular TLR3. Viral single-stranded RNA (e.g., SARS-CoV-2, hepato virus A, respiratory syncytial virus) stimulate intracellular TLR7. In animal models, sPTB can be induced by exposing gravid animals to high doses of bacteria, virus, or fungi or infusing PAMPs and DAMPs. In these experiments, knockout or disruption of TLR signaling rescues the animals from sPTB. In humans, polymorphisms in and differential methylation and expression of TLRs and molecules active in their signaling cascades have been consistently linked to sPTB. This is not surprising considering that intra- amniotic infection is present in approximately 10% of individuals with sPTB and intact membranes and vaginal infection is present in approximately 20% of individuals with sPTB, respectively. The most commonly isolated pathogens in sPTB include Ureaplasma. Candida, Fusobacterium, and E. coli species, which are associated with 1.3-2X higher risk for sPTB. Risk for sPTB also appears to be 1.6X higher during SARS-CoV-2 infection. Interestingly, while there are multiple TLRs active in human innate immune responsiveness, their pathways converge. Thus, interrogation of TLR4-, TLR2-, TLR3-. and TLR7-mediated responsiveness provides a comprehensive estimate of activity within TLR-mediated signaling cascades.

[0188] Immunomodulatory Effects of Progesterone. Levels of progesterone rise considerably across pregnancy. Functional progesterone withdrawal, through receptor isoform switching, is thought to play a role in the initiation of human labor. This has been studied primarily through direct assessment of myometrial tissues, as progesterone has anti -contractile properties. In fact, such data served as the basis for trials and then FDA approval of progesterone as a means to prevent sPTB in high-risk pregnancy. Though, the mechanism of action remained poorly understood. Now, results from a large, confirmatory trial raise serious doubt about progesterone’s effectiveness when broadly administered, as the drug appears to prolong pregnancy in some but not others for unknown reasons. Of note, progesterone is generally accepted as an immunosuppressive drug, as it’s been shown to dampen inflammation and pro-inflammatory responses to innate immune stimulation, potentially via TLR4 downregulation.

[0189] It is important to note that, first, human pregnancy differs considerably from that of other species. For example, mice and sheep experience absolute progesterone withdrawal while humans experience functional withdrawal. Second, sPTB is inherently different from medically initiated PTB. Yet, many studies collapse these phenotypes. Third, most available data in clinical samples has been collected during labor, with a case-control design. Thus, it’sexceedingly difficult to differentiate between mechanisms and downstream manifestations. Finally, most data reflect the steady state of cells and passively released byproducts. However, many of the alterations implicated in sPTB involve processes that are fluid and molecules that are constitutively expressed but rapidly upregulated under stimulatory conditions (e.g., TLRs).

[0190] Disclosed herein are methods of sampling live leukocytes from expectant mothers and challenging the function of innate immune pathways consistently implicated in sPTB and tested in vivo, either by pathogen- or damage-associated molecules. By quantifying risk for sPTB using a functional model, immunopathology can be diagnosed by ex vivo methods prior to in vivo insults, providing a glimpse into future events.

[0191] Furthermore, most cunent models of prenatal care focus on the treatment of overt signs and symptoms, including in sPTB. Based on this invention, it is now understood that these signs and symptoms are downstream manifestations of disease pathogenesis that represent a “point of no return.” Indeed, no treatment method has been successful in preventing sPTB after labor initiation. Broadly delivered interventions (e.g., progesterone, anti-inflammatory agents) have been administered with little consideration of individual differences in the underlying sPTB risk phenotype. Disclosed herein are methods of identifying individuals with a high likelihood of future sPTB by quantifying and modeling upstream indicators of risk and providing data toward the targeted prevention of sPTB based on prenatal immunomonitoring results.

[0192] Summary of Approach. A prospective cohort study of individuals presenting for the prenatal care of singleton pregnancy is carried out. Prenatal immunomonitoring methods are applied to whole blood biospecimens, profiling cytokine production following ex vivo TLR stimulation. Progesterone responsiveness is examined by quantifying the effect of progesterone on TLR-mediated functional immune responsiveness using an ex vivo, whole blood, doseresponse model. sPTB case-control status is determined by adjudicated review of manually abstracted medical records per ACOG guidelines.THEME 1. TLR-mediated functional immune responsiveness adapts across pregnancy, predicts future birth timing in a low-risk cohort, and predicts future sPTB in a moderate to high-risk cohort.

[0193] First, a traditional peripheral blood mononuclear cell immunomonitoring method was used in 76 uncomplicated pregnancies. Production of pro-inflammatory (i.e., IL-6, TNF-a, IL-ip, IL-8) cytokines was viewed. It was found that IL-6, TNF-a, and IL-ip production rose across pregnancy (p values<0.05) and declined postpartum (p values<0.03). It appeared that such adaptations may not only protect against fetal allograft rejection but may also prepare the maternal immune system to propagate the inflammatory events of labor via feedforward mechanisms.

[0194] Next, TLR4-mediated functional immune responsiveness at 28-32 weeks gestation and future birth timing in a low-risk cohort of 92 Black Americans was examined. A whole blood immunomonitoring method was applied to increase potential for clinical translation. Profiles of pro- (IL-6, TNF-a, IL-lp, IL-8) and anti-inflammatory (IL-10. TNFRI, TNFRII, ILIRa) cytokine production were examined. The final model explained 18.9% of the variance in future birth timing (R2=0.189, adj. R2=0.122), with independent associations between immune markers and birth timing remaining after controlling for sociodemographic, health behaviors, and clinical history (p values<0.037). Excluding those with medically initiated early birth (M=8), immune responsiveness explained 18.4% of the variance in future birth timing (R2=0.184, adj. R2=0.109). Importantly, quadratic associations were witnessed. As a comparator, concurrently assessed, passively measured immune markers were also examined, which predicted only 1.3% of the variance in future birth timing (R2=0.013, adj. R2=0.002).

[0195] Next, associations among TLR4-mediated functional immune responsiveness were examined at <20 weeks gestation and future sPTB in 81 individuals at moderate- to high-risk for sPTB. Again, a whole blood immunomonitoring method was applied. Informed by prior work, profiles of pro- (i.e., IL-6, TNF-a, IL-ip, IL-8) and anti-inflammatory (i.e., IL-10, TNFRI, TNFRII) cytokine production were examined and the panel was expanded to allow for further consideration of Thl (IL-6, TNF-a, IL- ip, IFN-y, IL-12p70) vs. Th2 (IL-10) responses, the Type II IFN response (IFN-y), and crosstalk between the innate and adaptive immune systems (IFN-y, IL-12p70). The final model predicted future sPTB with an accuracy of 97.5%, AUC of 0.93, sensitivity of 81.8%, specificity of 100.0%, positive predictive value (PPV) of 100.0%, negative predictive value of 97.2%, likelihood ratio positive (LR+) of 81.8, and likelihood ratio negative (LR-) of 0.18, with a probability threshold of 0.4 providing the greatest net benefit of risk classification. Again, quadratic associations were witnessed, showing hyper-inflammatory and hypo-responsive risk phenotypes.

[0196] As a comparator in evaluating the K23NR017902 cohort, future sPTB was modeled based on the history of PTB, history of sPTB, and history of more than one sPTB, whichprovided AUCs of 0.67, 0.69, and 0.58, respectively. Consistent with the extant literature, lack of PTB and lack of sPTB in prior pregnancy only provided value in ruling out potential for sPTB (PPVs of 0.0%, NPVs of 87.1%). More than one prior sPTB was required to provide information toward ruling in potential sPTB cases (PPV=50.0%, NPV=88.9%).THEME 2. Aberrant functional immune responses following the stimulation of TLRs may drive sPTB.

[0197] Associations among the epigenome-wide DNA methylation of maternal leukocytes sampled at 28-32 weeks gestation and future sPTB were also sampled in the low-risk cohort. A nested case-control design was applied, examining five sPTB cases and 11 matched, spontaneous full term birth controls. To increase the potential to produce biologically relevant findings, results were analytically triangulated and produced from three statistical methods, including a Bayesian curve credible band approach (BCurve) to account for correlation between methylation in nearby sites, covariates, and bctween-sample variability7. In doing so, 490 differentially methylated cytosines were identified. The top three differentially regulated networks, per Ingenuity Pathway Analysis, converged on predicted differential regulation of 1) P38 MAPK and ERK1 / 2, 2) Akt and JNK, and 3) PI3K, IKK, and NFKB. While these molecules are important to TLR4-mediated signaling (and lends support to the functional data), they are also key players in innate immune signaling via alternative pathways, with TLR2, TLR3, and TLR7 of particular interest.THEME 3. There is considerable inter-individual variation in the immunomodulatory effects of progesterone on prenatal TLR4-mediated immune responsiveness.

[0198] The immunomodulatory effect of progesterone on TLR4-mediated functional immune responsiveness w as quantified at <20 w eeks gestation in a pilot study of 27 individuals. Again, whole blood was challenged with lipopolysaccharide but added to the condition in which leukocytes were co-incubated with micronized progesterone at a dose commensurate to unmedicated, late pregnancy levels. Six individuals with competing outcomes were excluded (i.e., early medically initiated births) and compared immunomodulatory patterns among those with versus without spontaneous birth at <38 weeks gestation. It was hypothesized that variability would occur to the extent to which progesterone exerted an immunosuppressive effect. Instead, what appeared to be three phenotypes were seen: 1) unresponsive, 2) immunostimulatory, and 3) immunosuppressive (FIG. 5). It was also noted that individualswithout early spontaneous birth were "rescued" from the TLR4-mediated risk profiles described above when exposed to the high physiologic dose. It now appears that an unresponsive phenotype to high physiological doses of progesterone underlies and may even predict future sPTB risk. It was also hypothesized that by adding a supraphysiologic dosing condition (i.e.. that achieved during supplementation) and evaluating its ability to "rescue" responsiveness, one could identify individuals that would benefit from the drug.Study Design

[0199] A prospective cohort design is used among a diverse clinical cohort of individuals presenting for the prenatal care of singleton pregnancy.Recruitment & Retention

[0200] Eligibility Criteria. Individuals clinically presenting for prenatal care are considered for enrollment. Inclusion criteria includes singleton pregnancy diagnosed and dated by ultrasound at <16 weeks gestation. Exclusion criteria includes history of syncope during venipuncture or seizure disorder, determination of diminished capacity7without a legally authorized representative present, or maternal age <18 without a parent or legal guardian present. These criteria were chosen to reduce bias, maximize clinical applicability of findings, isolate the singleton sPTB phenotype, minimize attrition due to miscarriage, decrease potential for PTB misclassification, minimize risks to participants, and to promote inclusion while also protecting those with diminished autonomy. Participants were not excluded on the basis of chronic or pregnancy-related conditions or medication receipt so that the hypotheses in a clinically representative cohort could be tested with a range of risk profiles and potential measured and unmeasured covariates, maximizing the translational potential of the findings.Data Collection

[0201] Schedule. Patient-oriented data and biospecimens are collected at 14-16 weeks gestation, cervical length is measured at 18-22 weeks gestation, and medical record data is abstracted after birth among a diverse clinical cohort presenting for the prenatal care of singleton pregnancy (»=543).

[0202] Procedures. At 14-16 weeks gestation, questionnaires are administered to collect sociodemographic, health behavior, and clinical data. Objective indices of health are measured, including height, weight, body temperature, white blood cell count (3ml). and antinuclearantibody status (3ml). Heparinized whole blood (4ml) will be collected by venipuncture, allowing us to implement our prenatal immunomonitoring methods. During the 18-22 weeks gestation, all OSUWMC patients are offered cervical length measurement during the anatomical survey, with the vast majority of patients opting for measurement. We will record this data for our participants. After birth, additional clinical characteristics and birth outcome data are manually abstracted from prenatal and labor and delivery records, all of which are available through our OSUWMC Integrated Health Information System.Measurement

[0203] PhenX Toolkit. The Consensus Measures for Phenotypes and Exposures (PhenX) Toolkit is a web-based catalog of measures, protocols, worksheets, and data dictionaries carefully chosen and recommended by groups of domain experts. The PhenX Toolkit strengthens the measurement approach for each study and facilitates cross-study analysis, increasing scientific impact.

[0204] Participant Characteristics. Sociodeniographic. including social determinants of health, are assessed by self-report using the PhenX Demographics, Job Insecurity, Occupational Prestige, Food Insecurity, and Discrimination instruments. Census tract data is used to produce the PhenX Social Vulnerability, Air Quality', and Traditional Retail Food Environment indices. Health behaviors are assessed by self-report using the Prenatal Health Behavior Scale and the Pittsburgh Sleep Quality Index. Clinical characteristics are assessed by self-report using the PhenX Conditions Relevant to the Immune Response Instrument, supplemented by the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7. Clinical information is abstracted from manual medical record reviews using the PhenX Medical History. Medication Inventory, Reproductive History’, Pregnancy, Mode of Delivery, and Gestational Age Instruments, supplemented by recording interventions and the Obstetric Complications Scale, Severe Maternal Morbidity ICD Indicators, and Postnatal Complications Scale. Objective indices of health are measured and recorded using the PhenX Anthropometries and Tympanic Body Temperature Instruments. Clinical laboratory' results are abstracted from medical records of prenatal and intrapartum care. Complete blood counts are performed with electronic differential using volume, conductivity, and light scatter technology and antinuclear antibody multiplex screens by indirect fluorescent antibody at OSUWMC clinical laboratory at the time of venipuncture for immunomonitoring. Cervical lengthscreening is performed by transvaginal ultrasound at the time of anatomical screening at 18-22 weeks gestation, producing a quantitative measure of length and a qualitative measure of sPTB risk per ACOG level B recommendation (i.e., <25mm).

[0205] Prenatal Immunomonitoring. TLR-mediated functional immune responsiveness is assessed as profiles of cytokine production following ex vivo, whole blood TLR4 stimulation using lipopolysaccharide. Specifically, within 30 minutes of venipuncture at the clinical site, heparinized whole blood will be added to quality controlled microtubes containing RPMI 1640 Medium and standardized lipopolysaccharide concentration, using clean technique. Microtubes are placed in portable incubators at the clinical site. Daily, incubators are transferred to OSUCON Biomedical Research Laboratory. After standardized incubation period, microtubes are centrifuged at 1000g x 5 minutes, and supernatants will be aspirated, aliquoted, and stored at -80°C. In batches, supernatants will be assayed in duplicate by multiplex electrochemiluminescence to quantify IL-6, TNF-a, IL- 1 p, IL-8, IL-10, TNFRI, TNFRIL IFN- y, IL-2. !L-12p70, IL-4, MIP and IL-13 using kits from Meso Scale and the MESO QuickPlex SQ 120 per manufacturer instructions. Intra-assay, inter-assay, and inter-lot CVs are <5%, <7%, and <6% across these analytes per manufacturer data, respectively, which is consistent with the laboratory ’s results. Next, TLR2-. TLR3-, and TLR7-mediated functional immune responsiveness is assessed by repeating the above steps, with zymosan, Poly(I:C), and gardiquimod serving as stimulants, respectively. Again, supernatant cytokine levels are assayed, in duplicate, by multiplex electrochemiluminescence. The immunomodulatory effect of progesterone on TLR-mediated functional immune responsiveness is assessed by quantifying the effect of progesterone on TLR4-mediated functional immune responsiveness using an ex vivo, whole blood, dose-response model. As above, whole blood is incubated with lipopolysaccharide, but in the presence of high and supraphysiologic doses of progesterone. For each condition, supernatant cytokine levels are assayed in duplicate, by multiplex electrochemiluminescence. The change in cytokine level is assessed as leukocytes are exposed to increasing doses of progesterone to identify non-responses, immunostimulatory responses, and immunosuppressive responses for each assessed cytokine.

[0206] Spontaneous Preterm Birth. Cases are sPTBs identified by manual medical record review, defined per the criteria outlined in ACOG Practice Bulletins, Number 171, 172, and 234. Cases include PTBs initiated by preterm labor and / or preterm premature rupture of membranes, with or without vaginal bleeding. Cases are compared to controls, which aredefined as all other peri viable or viable births at or beyond 20 weeks gestation, including medically initiated PTBs. The rationale for these decisions is to isolate future sPTB cases from all other births, boosting the abi 1 i ty to identify and address the sPTB phenofype.Data Analysis

[0207] Data Management. During the clinical encounter, patient-oriented data is directly entered by participants into our Research Electronic Data Capture (REDCap) database using iPads. After birth, detailed prenatal and labor and delivery medical record data is manually abstracted and directly entered by research staff into REDCap. The sPTB case versus control designation of each birth is adjudicated by clinical investigative team. Data is stored securely and backed up regularly on both the cloud-based REDCap platform and using our local research drive. Data is carefully reviewed and cleaned. Data is examined for missingness and the nature of missingness. Data distributions of primary variables are examined, and transformations are applied as appropriate.

[0208] Data Analysis Plan. A prenatal algorithm of TLR-mediated functional immune responsiveness is tested for the prediction of future sPTB in a diverse clinical cohort. The immunomodulatory effects of progesterone on TLR4-mediated functional immune response are calculated and the association between this profile and future sPTB is examined. The goal is to produce a robust clinical prediction model for the binary outcome of sPTB using prenatal immunomonitoring data generated from 14-16 week biospecimens. Quantitative indices of TLR4-, TLR2-, TLR3-, and TLR7-mediated cytokine production serve as predictors. The immunomodulatory effect of progesterone on TLR4-mediated cytokine production also serves as predictors, with participants categorized as non-responder, immunostimulatory responders, or immunosuppressive responders four pro-inflammatory and three anti-inflammatory cytokines.

[0209] Model selection is accomplished using LASSO regularization for variable selection, 10-fold cross-validation for resampling, and multiple logistic regression for classification. Model assessment is accomplished in two steps. First, the performance of the derived immunomonitoring algorithms is examined by conducting a net benefit analysis and examining the Youden index to identify optimal probability cut points and examining threshold independent and dependent indices - i.e., area under the receiver operating characteristic curve (AUC), predictive accuracy, sensitivity, specificity, positive (PPV) and negative (NPV)predictive values, and positive (LR+) and negative (LR-) likelihood ratios. The AUC is a graphical representation and summary statistic representative of the balance between sensitivity and specificity at various cut points. Sensitivity is the likelihood that patients with future sPTB can have a positive test result. Specificity is the likelihood that patients without future sPTB can have a negative test result. PPV is the proportion of individuals with a positive test that have a future sPTB. NPV is the proportion of individuals with a negative test result that do not have a future sPTB. LR+ and LR- are perhaps the most clinically informative parameters, as they reflect the degree of change in the pre- to post-test probability. For example, an LR+ of 10 would indicate a 10-fold increase in the odds of future sPTB in an individual with a positive test. An LR- of 0. 1 would indicate a 10-fold decrease in the odds of future sPTB in an individual with a negative test. Next, comparison of model performance is done to the performance of current, clinically recommended sPTB predictive algorithms. This includes sPTB prediction with sPTB history in prior singleton pregnancy serving as the predictor (Level A Recommendation) and sPTB prediction with 18-22 week cervical length serving as the predictor (Level B Recommendation). This is primarily accomplished by testing the equality of the obtained AUCs using the process of DeLong and colleagues.Findings

[0210] Study 1. A high-performing algorithm of TLR4-mediated functional immune responsiveness can be generated for sPTB prediction. Potential contributions of TLR2. TLR3, and TLR7 -mediated parameters are used, which can increase sensitivity by identifying risk via related mechanisms not captured by TLR4 stimulation. We achieved an AUC of 0.93, sensitivity of 81.8%, specificity of 100.0%, LR+ of 81.8, and LR- of 0.18 examining TLR4- mediated responsiveness alone.

[0211] Study 2. Progesterone non-response, and immunostimulatory and immunosuppressive response can be identified by assessing cytokine production during exposure to high physiologic doses compared to a control condition. Nonresponse at high physiological doses is associated with higher odds of future sPTB. The supraphysiologic doses of progesterone can "rescue’' non-responsive profiles, potentially identifying candidates for preventive therapy in biomarker strategy RCTs of targeted progesterone allocation.

[0212] Immediate Applications. Disclosed herein is the production of novel insights into the pathogenesis, prediction, and prevention of sPTB, a common and devastating syndrome.Prenatal TLR-mediated functional immune responsiveness is used as an actionable target toward sPTB clinical prediction and prevention. The design of a biomarker-strategy is informed for RCT testing the efficacy of immunobiologically-directed progesterone allocation, providing a direct avenue toward targeted prevention of sPTB using a drug known to be safe in pregnancy.

[0213] Connections to Practice. In the clinical setting, risk prediction and targeted prevention go hand-in-hand, making advancements in one area dependent upon the capacity to advance the other. Disclosed herein are methods used to identify individuals at risk for sPTB by diagnosing key drivers of dominant sPTB risk phenotypes. TLR-mediated functional immune responsiveness and progesterone non-responsiveness. Progesterone is an attractive option as a targeted preventive therapy, as the drug has been extensively studied in pregnancy and appears to be beneficial in some but not others. The findings disclosed herein can also identity7novel targets for sPTB prevention, as targeted immunomodulation has been highly successful in other lines of research. In sum, this project could provide an unprecedented opportunity to predict and prevent sPTB.References for Example 2:• ACOG Practice Bulletin No. 142: Cerclage for the management of cervical insufficiency. 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[0214] Venous whole blood is collected. Alternatively, capillary' blood can be used. In one example. 6 ml of blood is collected, but any volume can be used. Heparinized vacutainers can be used to anti-coagulate blood. Any method of anticoagulation could work, or even uncoagulated blood. Samples can be collected during pregnancy, pre-pregnancy, or even postpartum.Step 2: Blood is Transferred

[0215] Accuracy and reproducibility of the test are optimized if this process is sterile (i.e., it does not introduce germs) but can also be completed using clean techniques. Sterile transfercan be accomplished using sterile supplies and a hood. Sterile transfer can also be accomplished using a closed system.Step 3: Blood is Stimulated

[0216] Venous whole blood is stimulated by adding the blood to pre-prepared microtubes containing standardized amounts of lipopolysaccharide, which is a toll-like receptor (TLR) 4 ligand that stimulates activation of TLR4-mediated leukocyte signaling.

[0217] TLR2 (ligand = zymosan), TLR3 (ligand = Poly(I:C)), and TLR7 (ligand = gardiquimod) stimulation can also be used, which provides broad coverage of TLR-mediated innate immune responsiveness. TLR1, TLR3, TLR5, TLR6. TLR8, TLR9, and TLR10 can also be used. TLRs can be stimulated in a number of ways, including novel fabricated molecules. TLRs are pattern recognition receptors (PRRs). PRRs are critical to innate immunity and direct the adaptive immune response. PRRs fall into five classes: TLRs; AIM2-like receptors (ALRs); C-type lectin receptors (CLRs); Retinoic acid-inducible gene (RIG) I-like receptors (RLRs); NOD-like receptors (NLRs).Step 4: Incubation of Stimulated Blood

[0218] Accuracy and reproducibility' of the test are optimized when the blood is stimulated and reaches the incubator quickly, such as in less than one hour. The test can work with different timing parameters. Incubation can occur, for example, for 4 hours at 37 degrees C. The test can work over a range of times and temperatures. One of skill in the art will appreciate how to optimize these ranges.Step 5: Supernatants are Separated

[0219] Stimulation preparations are centrifuged for 10 minutes at room temperature to separate the supernatant from the cells and platelets. This can be done in a number of different manners known to those of skill in the art.Step 6: Supernatants are Stabilized

[0220] Supernatants are stabilized by aspirating them, aliquoting them into separate microtubes, and placing them at 4 degrees C for short term storage and then -20 or -80 degrees C for long-term storage.Step 7 : Supernatant Inflammatory Mediators are Quantified

[0221] A panel of pro- and anti-inflammatory cytokines and chemokines are quantified using the Meso Scale Discovery multiplex immunoassays. Key analytes include : IFN-y, IL-1 [3, IL- 2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, TNF-a, TNFRI, and TNFRII. Though, there are ahost of cytokines, chemokines, and alternative analytes (e.g., metaloproteinases) that could contribute to or improve prediction.Step 8: Supernatant Profiles are Analyzed for the Prediction of PTB and / or HDPs of Pregnancy and Postpartum

[0222] Linear and non-linear relationships are monitored, with supernatant profiles serving as the predictor of PTB and / or HDPs and postpartum serving as the outcome. The strongest model is for the prediction of spontaneous preterm birth. The performance of this model is optimized when including parameters reflective of white blood cell counts as predictors as well. Supernatant profiles are also modeled for the prediction of hypertensive disorders of pregnancy, which is optimized when including platelet counts. Stimulation-based models for the prediction of preterm birth can also be used (includes spontaneous and medically induced), hypertensive disorders of pregnancy, gestational diabetes, intrauterine growth restriction, low birth weight birth, severe maternal morbidity’, fetal inflammatory response syndrome, need for neonatal intensive care, prenatal and postpartum depression, and prenatal and postpartum anxiety.References for Example 3:• Pregnancy Mortality Surveillance System | Maternal and Infant Health | CDC. Published August 25, 2022. Accessed November 30, 2022. https: / / www.cdc.gov / reproductivehealth / matemal-mortality / pregnancy-mortality-surveillance-system htm• Chawanpaiboon S, Vogel JP, Moller AB, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health. 2019;7(l):e37-e46. doi: 10.1016 / S2214-109X(18)30451-0• US Burden of Disease Collaborators, Mokdad AH, Ballestros K, et al. The State of US Health, 1990-2016: Burden of Diseases, Injuries, and Risk Factors Among US States. JAMA. 20I8;319(I4): 1444-1472. doi:10.1001 / jama.2018.0158• Heron M. Deaths: Leading Causes for 2019. Natl Vital Stat Rep Cent Dis Control Prev Natl Cent Health Stat Natl Vital Stat Syst. 2021 ;70(9): 1-114.• GBD 2015 Child Mortality Collaborators. Global, regional, national, and selected subnational levels of stillbirths, neonatal, infant, and under-5 mortality. 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Lond Engl.2016;388(10053): 1725-1774. doi: 10. 1016 / S0140-6736(16)31575-6• GBD 2016 Mortality Collaborators. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Lond Engl. 2017;390(10100): 1084- 1150. doi: 10.1016 / S0140-6736(17)31833-0• Li Y, Fu X, Guo X, Liang H, Cao D, Shi J. Maternal preterm birth prediction in the United States: a case-control database study. BMC Pediatr. 2022;22(l):547. doi: 10. 1186 / S12887-022-03591• Chen W, Guo Y, Yao X. Zhao D. Correlation of Blood Lipid and Serum Inflammatory Factor Levels With Hypertensive Disorder Complicating Pregnancy. Front Surg. 2022;9:917458. doi: 10.3389 / fsurg.2022.917458• Ju Y, Feng Y, Yang Y, et al. Combining curcumin and aspirin ameliorates preeclampsia-like symptoms by inhibiting the placental TLR4 / NF-KB signaling pathway in rats. J Obstet Gynaecol Res. Published online October 26, 2022. doi: 10. 1111 / jog. 15473• Hu J, Guo Q, Liu C, et al. Immune cell profiling of preeclamptic pregnant and postpartum women by single-cell RNA sequencing. Int Rev Immunol. Published online November 11, 2022: 1-12. doi: 10.1080 / 08830185.2022.2144291• Mora-Palazuelos C, Bermudez M, Aguilar-Medina M, Ramos-Payan R, Ayala-Ham A, Romero-Quintana JG. Cytokine-polymorphisms associated with Preeclampsia: A review. Medicine (Baltimore). 2022;101(39):e30870. doi:10.1097 / MD.0000000000030870• Moldenhauer LM, Hull ML, Foyle KL, McCormack CD, Robertson SA. Immune- Metabolic Interactions and T Cell Tolerance in Pregnancy. J Immunol Baltim Md 1950. 2022:209(8): 1426-1436. doi: 10.4049 / jimmunol.2200362

Claims

CLAIMS1. A method of determining likelihood of preterm birth (PTB) or hypertensive disorders of pregnancy (HDPs) in a subject, the method comprising obtaining a sample from a subject, assaying immune markers in the sample to determine status of immune markers in the subject, and comparing status of immune markers to a control using an algorithm, thereby determining the likelihood of PTB and / or HDPs.

2. The method of claim 1, wherein the immune markers are toll-like receptors (TLR)- mediated.

3. The method of claim 2, wherein the TLR-mediated immune response is measured by determining status of TLR1, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, or TLR10.

4. The method of claim 2 or 3, wherein the immune markers are proinflammatory cytokines or anti-inflammatory cytokines.

5. The method of claim 4, wherein the pro-inflammatory cytokines are selected from the group comprising interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), interleukin- 1 beta (IL-1 P), interleukin-8 (IL-8), macrophage inflammatory proteins (MIPs), monocyte chemoattractant proteins (MCPs), and interleukin- 17 (IL- 17).

6. The method of claim 4, wherein the anti-inflammatory cytokines are selected from the group comprising interleukin- 10 (IL- 10), tumor necrosis factor receptor I (TNFRI), tumor necrosis factor receptor II (TNFRII), interferon-gamma (IFN-y), interleukin- 12p70 (IL- 12p70), interleukin- 13 (IL-13), interleukin-37 (IL-37), transforming growth factors (TGFs), resolvins, and protectins.

7. The method of any one of claims 1-6, wherein the subject who is determined as having the likelihood of PTB and / orHDPs is subsequently treated to prevent PTB and / orHDPs, or monitored more frequently than a subject who has not been determined as having the likelihood of PTB and / orHDPs, or counseled to modify behavior to reduce risk of PTB and / orHDPs.

8. The method of claim 7, wherein said treatment method comprises administration of progesterone or administration of a higher level of progesterone in subjects already being given progesterone.

9. The method of claim 7 or 8, wherein said treatment method comprises administration of an immunomodulatory' agent.

10. The method of any one of claims 1-9, wherein a PTB and / orHDPs risk score is obtained based on marker levels as compared to a control.

11. The method of claim 10, wherein the PTB and / orHDPs risk score is used to place the subject in a risk group.

12. The method of any one of claims 1-11, wherein one or more additional factors are also used to determine an increased risk of PTB and / orHDPs.

13. The method of claim 12, wherein said additional factors include lifestyle-associated risks.

14. The method of claim 12, wherein said additional factors include family history.

15. The method of claim 12, wherein said additional factors include previous PTB and / or HDPs by the subj ect.

16. The method of claim 12, wherein said additional factors include medical conditions or genetic factors.

17. The method of claim 12. wherein said additional factors include determination of methylation status.

18. The method of any one of claims 1-17, wherein the subject is not pregnant when assaying markers is carried out.

19. The method of any one of claims 1-17, wherein the subject is pregnant when assaying markers is carried out.

20. The method of any one of claims 1-19, wherein the control is a predetermined value.

21. The method of any one of claims 1-20, wherein the control is from a sample obtained from the subject at a previous time point.

22. The method of any one of claims 1-21, wherein the sample is a blood sample.

23. The method of claims 22, where whole blood immunomonitoring is used.

24. The method of any one of claims 2-3, wherein the TLR is stimulated.

25. The method of claim 24, wherein the TLR is stimulated by exposing the sample to a TLR stimulant.

26. The method of any one of claims 1-25, wherein preterm birth (PTB) is spontaneous preterm birth, medically indicated preterm birth, or iatrogenetic preterm birth.

27. The method of any one of claims 1-25, wherein hypertensive disorder of pregnancy (HDP) is gestational hypertension, preeclampsia, eclampsia, HELLP syndrome, chronic hypertension during pregnancy or superimposed preeclampsia.

28. A method of determining likelihood of PTB and / orHDPs in a subject the method comprising obtaining a sample from a subject and determining methylation status of at least one marker involved in immune response, and comparing methylation status of the subject to a control, thereby determining the likelihood of PTB and / orHDPs.

29. The method of claim 28. wherein the one or more markers involved in immune response are related to toll-like receptor (TLR) signaling.

30. The method of claim 29, wherein the TLR signal relates to TLR1, TLR2, TLR3, TLR4, TLR5, TLR6. TLR7, TLR8, TLR9, or TLR10.

31. The method of claim 30, wherein the methylation marker(s) relate to at least one of AP-1, NFKB, or PI3K signaling.

32. The method of any one of claims 28-31, wherein the subject who is determined as having the likelihood of PTB and / orHDPs is subsequently treated to prevent PTB and / orHDPs, or monitored more frequently than a subject who has not been determined as having the likelihood of PTB and / orHDPs, or counseled to modify behavior to reduce risk of PTB and / orHDPs.

33. The method of claim 32, wherein said treatment method comprises administration of progesterone or administration of a higher level of progesterone in subjects already being given progesterone.

34. The method of claim 32 or 33, wherein said treatment method comprises administration of an immunomodulatory agent.

35. The method of any one of claims 28-34, wherein a PTB and / orHDPs risk score is obtained based on marker levels as compared to a control.

36. The method of claim 35, wherein the PTB and / orHDPs risk score is used to place the subject in a risk group.

37. The method of any one of claims 28-36, wherein one or more additional factors are also used to determine an increased risk of PTB and / orHDPs.

38. The method of claim 37. wherein said additional factors include lifestyle-associated risks.

39. The method of claim 37, wherein said additional factors include family history.

40. The method of claim 37, wherein said additional factors include previous adverse outcome by the subject.

41. The method of claim 37, wherein said additional factors include medical conditions or genetic factors.

42. The method of claim 37, wherein said additional factors include determination of status of immune markers in the subject.

43. The method of claim 42. wherein the immune markers are proinflammatory cytokines or anti -infl ammatory cytokines.

44. The method of claim 43, wherein the pro-inflammatory cytokines are selected from the group comprising interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), interleukin- 1 beta (IL- 1 P), interleukin-8 (IL-8), macrophage inflammatory proteins (MIPs), monocyte chemoattractant proteins (MCPs), and interleukin- 17 (IL- 17).

45. The method of claim 43, wherein the anti-inflammatory cytokines are selected from the group comprising interleukin- 10 (IL- 10), tumor necrosis factor receptor I (TNFRI), tumor necrosis factor receptor II (TNFRII), interferon-gamma (IFN-y), interleukin- 12p70 (IL- 12p70), interleukin- 13 (IL- 13), interleukin-37 (IL-37), transforming growth factors (TGFs), resol vins, and protectins.

46. The method of any one of claims 42-45, wherein status of immune markers in the subject are determined using the method of any one of claims 1-27.

47. The method of any one of claims 28-46, wherein the subject is not pregnant when assaying markers is carried out.

48. The method of any one of claims 28-46, wherein the subject is pregnant when assaying markers is carried out.

49. The method of any one of claims 28-48, wherein the control is a predetermined value.

50. The method of any one of claims 28-48, wherein the control is from a sample obtained from the subject at a previous time point.

51. The method of any one of claims 28-50, wherein the sample is a blood sample.

52. The method of claim 51, where whole blood immunomonitoring is used.

53. A kit comprising markers for determining status of immune markers in a subject, wherein the markers are toll-like receptor (TLR)-mediated.

54. The kit of claim 53, wherein the TLR-mediated immune response is measured by determining status of TLR1, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, or TLR10.

55. The kit of claim 53 or 54, wherein the immune marker(s) are pro-inflammatory cytokines or anti-inflammatory cytokines.

56. The kit of claim 53, wherein the pro-inflammatory markers are selected from the group comprising IL-6, TNF-a, IL-ip. IL-8. MIPs, MCPs, and IL-17.

57. The kit of claim 53, wherein the anti-inflammatory markers are selected from the group comprising IL-10, TNFRI, TNFRII, IFN-y, IL-12p70, IL-13, IL-37, TGFs, resolvins, and protectins.

58. The kit of any one of claims 53-57, wherein the kit comprises one or more stimulants of a toll-like receptor.

59. The kit of claim 58, wherein the one or more stimulants is a ligand for the toll-like receptor.

60. The kit of claim 59, wherein the ligand stimulant is lipopolysaccharide, zy mosan, Poly(I:C), or gardiquimod.

61. The kit of any one of claims 53-60. wherein the kit comprises an incubator.

62. The kit of any one of claims 53-61, wherein the kit comprises a multiplex immunoassay.

63. A method of determining preterm birth (PTB) or hypertensive disorders of pregnancy (HDPs) in a subject, comprising: obtaining a sample from the subject; stimulating the sample with an immunogen; detecting a level of at least one immune marker, wherein the immune marker comprises pro-inflammatory cytokines or anti-inflammatory’ cytokines; and determining the subject is at an increased risk of PTB and / or HDP if the level of at least one immune marker is above a probability threshold level.

64. The method of claim 63, further comprising determining a risk category’ based on the probability threshold level, wherein the probability threshold level of <0.2 indicates a low- risk category, the probability threshold level of 0.2-0.4 indicates a moderate-risk category, the probability threshold level of >0.4 indicates a high-risk category, wherein the subject is in the high-risk category’ if the subject has elevated levels of at least one immune marker as compared to a control.

65. The method of any one of claims 63 or 64. wherein the at least one immune marker is toll-like receptors (TLR)-mediated.

66. The method of claim 65, wherein the TLR-mediated immune response is measured by determining status of TLR1, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, or TLR10.

67. The method of claim 63, wherein the pro-inflammatory cytokines are selected from the group comprising interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), interleukin- 1 beta (IL-ip), interleukin-8 (IL-8), macrophage inflammatory proteins (MIPs), monocyte chemoattractant proteins (MCPs), and interleukin- 17 (IL- 17).

68. The method of claim 63, wherein the anti-inflammatory’ cytokines are selected from the group comprising interleukin- 10 (IL- 10), tumor necrosis factor receptor I (TNFRI), tumor necrosis factor receptor II (TNFRII), interferon-gamma (IFN-y), interleukin- 12p70 (IL-12p70), interleukin- 13 (IL-13), interleukin-37 (IL-37), transforming growth factors (TGFs), resolvins, and protectins.

69. The method of any one of claims 63-68. wherein one or more additional factors are also used to determine an increased risk of PTB and / orHDPs.

70. The method of claim 69, wherein said additional factors include lifestyle-associated risks.

71. The method of claim 69, wherein said additional factors include family history.

72. The method of claim 69, wherein said additional factors include previous PTB and / or HDPs by the subject.

73. The method of claim 69, wherein said additional factors include medical conditions or genetic factors.

74. The method of claim 69, wherein said additional factors include determination of methylation status and / or gene expression status.

75. The method of any one of claims 63-74, wherein the subject is not pregnant w hen assaying markers is carried out.

76. The method of any one of claims 63-74, wherein the subject is pregnant when assaying markers is carried out.

77. The method of any one of claims 63-76, wherein the control is from a sample obtained from the subject at a previous time point.

78. The method of any one of claims 63-77, wherein the sample is a blood sample.

79. The method of claims 78, where whole blood immunomonitoring is used.

80. The method of any one of claims 63-66, wherein the TLR is stimulated by exposing the TLR to the immunogen.

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