RNA modulation of a pregnancy-related state of a subject

Abstract

The present disclosure provides methods and compositions for modulating an mRNA expression level of a pregnant subject. A method may comprise modulating a transcription factor of the pregnant subject, thereby treating the pregnant subject for a pregnancy-related state.

Description

[0001] RNA MODULATION OF A PREGNANCY-RELATED STATE OF A SUBJECT

[0002] CROSS-REFERENCE

[0003] [1] This application claims the benefit of U.S. Provisional Application No. 63 / 631,104, filed April 8, 2024, which is incorporated by reference herein in its entirety.

[0004] BACKGROUND

[0005] [2] Every year, about 15 million pre-term births as well as 10 million cases of preeclampsia are reported globally, and over 300,000 women die of pregnancy related complications such as hemorrhage and hypertensive disorders like preeclampsia. Preeclampsia may affect as many as about 8% of pregnancies, and is a leading cause of maternal mortality and morbidity. Pregnancy-related complications such as preeclampsia are a leading cause of maternal death and premature birth for baby followed by complications later in life. Further, such pregnancy -related complications can cause negative health effects on maternal health.

[0006] SUMMARY

[0007] [3] Currently there are no known cures for preeclampsia. Preeclampsia can vary in severity from mild to life threatening. A mild form of pre-eclampsia can be treated with bed rest and frequent monitoring. For moderate to severe cases, hospitalization is recommended and blood pressure medication or anticonvulsant medications to prevent seizures are prescribed. If the condition becomes life threatening to the mother or the baby the pregnancy is terminated and the baby is delivered pre-term.

[0008] [4] Clinical manifestations of hypertension and proteinuria that define preeclampsia probably represent the late stage of a disease that begins very early in pregnancy. There are multiple theories, and little agreement, about the ultimate cause of preeclampsia, and it is likely that many different initial insults converge on a common pathophysiology (or two common pathophysiologies, if considering early- and late-onset preeclampsia separately). However, what is clear is that all forms of the disease are characterized by a disruption of vascular remodeling and a systemic anti-angiogenic response. The underlying mechanisms contributing to these changes remain unclear and might overlap. Among the possible mechanisms are alterations in the maternal immune response to the allogenic fetus and placental oxygen dysregulation (including inappropriate placental hypoxia and hypoxia-reoxygenation injury).

[0009] [5] Morten Rasmussen at al. (“RNA profiles reveal signatures of future health and disease in pregnancy”, Nature, 601(7893), 422-427, January 5, 2022, which is incorporated by reference herein in its entirety) and Moufarrej et al. (“Early prediction of preeclampsia in pregnancy with cell-free RNA”, Nature, 602, 689-694, February 9, 2022, which is incorporated by reference herein in its entirety) showed that the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia. cfRNA profiling indicates at least two separate groups of genes and pathways are dysregulated at early stages of preeclampsia. Placental and kidney associated PAPPA2 gene expression is upregulated in the plasma of mothers at early stages of pregnancy and predictive of mothers developing a severe form of preeclampsia, and immune balance related CD 163 gene is predictive of mothers developing a mild form of preeclampsia.

[0010] [6] Pregnancy-associated plasma protein A2 (PAPPA2) is an insulin-like growth factorbinding protein (IGFBP) protease expressed at high levels in the placenta and upregulated in pregnancies complicated by preeclampsia and HELLP (Hemolytic anemia, Elevated Liver enzymes, and Low Platelet count) syndrome. However, it is unclear whether elevated PAPPA2 expression causes abnormal placental development, or whether upregulation compensates for placental pathology. In the present study, we investigate whether PAPPA2 expression is affected by hypoxia, oxidative stress, syncytialization factors or substances known to affect the expression of PAPPA2's paralogue, PAPPA (Wagner et al., “Regulation of pregnancy- associated plasma protein A2 (PAPPA2) in a human placental trophoblast cell line (BeWo)”, Reprod Biol Endocrinol, 2011 Apr 15:9:48, which is incorporated by reference herein in its entirety).

[0011] [7] PAPPA2 and genetic variations are associated with salt sensitivity, blood pressure changes and hypertension in human (Wang et al., “Associations of plasma PAPP-A2 and genetic variations with salt sensitivity, blood pressure changes and hypertension incidence in Chinese adults”, Journal of Hypertension, 39(9): 1817-1825, September 2021, which is incorporated by reference herein in its entirety).

[0012] [8] Preeclampsia (PE) is a pregnancy-specific syndrome characterized by a systemic inflammatory response that polarizes peripheral blood monocytes to the Ml phenotype. The classically activated Ml monocytes comprise immune effector cells with an acute inflammatory phenotype. CD 163 is a scavenger receptor expressed by monocytes / macrophages that may be shed from their cell membrane after proteolytic cleavage, producing the soluble CD 163 molecule (sCD163).

[0013] [9] Careful regulation of PAPP A2 and CD 163 signaling pathways is critical for maintaining appropriate proliferation, migration, and angiogenesis by trophoblast cells in the developing placenta and immune responses. There is a need for methods of accurately diagnosing subjects at risk for or having preeclampsia associated with different subtypes of pathway dysregulation. A specific treatment targeting specific pathways may be needed for better treatment.

[0010] Currently, there may be a lack of meaningful, clinically actionable diagnostic screenings or tests available for many pregnancy-related complications such as pre-term birth, preeclampsia, and growth restriction of the fetus. Antenatal screening tools may fail to detect up to 70% of pregnancies with a growth restricted fetus, therefore failing to identify those at greatest risk for adverse outcomes, including fetal mortality.

[0014]

[0011] Thus, to make pregnancy as safe as possible, there exists a need for rapid, accurate methods for identifying specific subtypes of preeclampsia and applying specific treatment according to subtype of preeclampsia prior to development of preeclampsia.

[0015]

[0012] Using cfRNA expression analysis, we have discovered that cfRNA levels of PAPP A2 and CD 163 are elevated in plasma from pregnant women who are at high risk of developing two distinctive subtypes of preeclampsia. Modulation of PAPP A2 and CD 163 gene expression or protein levels to normal level may also lead to reducing the risk of developing preeclampsia.

[0016]

[0013] Small interfering RNAs (siRNAs) may refer to 10-21 nt long or 21-23 nt long doublestranded oligoribonucleotides, which in mammalian cells exhibit a potency for sequence-specific gene silencing via an RNA interference (RNAi) pathway. SiRNA can be used to modulate the levels of genes, such as PAPPA2 and CD 163. Antisense oligonucleotides (ASOs) may refer to single-stranded, chemically modified DNA or RNA molecules that bind to complementary messenger RNA (mRNA) sequences, thereby blocking protein synthesis or triggering RNA degradation.

[0017]

[0014] ASOs and / or siRNAs may be used for therapeutic applications in various pregnancy- related conditions, such as preeclampsia. ASOs and siRNAs may be highly specific for targeted RNA modulation and gene silencing, can be designed to target one or multiple mRNA transcripts simultaneously, and may be simple to synthesize and chemically modify. The RNA modulation therapy may comprise suitable approaches for delivery to specific cells or tissues (e.g., placental tissue), minimizing off-target effects, and minimizing immune responses or toxicity.

[0018]

[0015] The present disclosure provides for the use and monitoring of PAPP A2 and CD 163 as detection tools for early testing and management of preeclampsia or eclampsia and providing targeted and specific treatment based on test outcome.

[0019]

[0016] The present disclosure provides methods, systems, and kits for identifying or monitoring pregnancy-related states by processing cell-free biological samples obtained from or derived from subjects. Cell-free biological samples (e.g., plasma samples) obtained from subjects may be analyzed to identify the pregnancy-related state (which may include, e.g., measuring a presence, absence, or relative assessment of the pregnancy -related state). Such subjects may include subjects with one or more pregnancy -related states and subjects without pregnancy- related states. Pregnancy-related states may include, for example, pre-term birth, full-term birth, gestational age, due date (e.g., due date for an unborn baby or fetus of a subject), onset of labor, cholestasis, ligohydramnios, polyhydramnios, pregnancy-related hypertensive disorders (e.g., preeclampsia), eclampsia, HELLP syndrome, gestational diabetes, a congenital disorder of a fetus of the subject, ectopic pregnancy, spontaneous abortion, stillbirth, antepartum and intrapartum fetal demise, intrauterine fetal demise, post-partum complications (e.g., post-partum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during delivery, premature rupture of membrane, premature rupture of membrane in pre-term birth, prelabor preterm rupture of membranes, placenta accreta spectrum disorders and placental abruption, placenta previa (placenta covering the cervix), intrauterine growth restriction, fetal growth restriction, small for gestational age (SGA), macrosomia (large fetus for gestational age), neonatal conditions (e.g., neonatal alloimmune thrombocytopenia and neonatal autoimmune thrombocytopeni, fetal genetic syndromes, inborn errors of metabolism, anemia, apnea, bradycardia and other heart defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis (e.g., abdominal wall defects including omphalocele and others) hydrocephaly, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and fetal development stages or states (e.g., normal fetal organ function or development, and abnormal fetal organ function or development). For example, the fetal development stages or states may be related to normal fetal organ function or development and / or abnormal fetal organ function or development for a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus.

[0020]

[0017] In an aspect, the present disclosure provides a method for identifying a presence or elevated risk of a pregnancy-related state of a pregnant subject, comprising assaying a cell-free biological sample derived from the pregnant subject to detect a set of biomarkers, and processing the set of biomarkers with a trained algorithm or against a reference value to determine the presence or elevated risk of the pregnancy-related state among a set of at least three distinct pregnancy-related states.

[0021]

[0018] In some embodiments, the pregnancy-related state is selected from the group consisting of pre-term birth, full-term birth, gestational age, due date (e.g., due date for an unborn baby or fetus of a subject), onset of labor, cholestasis, oligohydramnios, polyhydramnios, pregnancy- related hypertensive disorders (e.g., preeclampsia), eclampsia, HELLP syndrome, gestational diabetes, a congenital disorder of a fetus of the subject, ectopic pregnancy, spontaneous abortion, stillbirth, antepartum fetal demise, intrapartum fetal demise, intrauterine fetal demise, post-partum complications (e.g., post-partum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during delivery, premature rupture of membrane, premature rupture of membrane in pre-term birth, prelabor preterm rupture of membranes, placenta accreta spectrum disorders and placental abruption, placenta previa (placenta covering the cervix), intrauterine growth restriction, fetal growth restriction, small for gestational age (SGA), macrosomia (large fetus for gestational age), neonatal conditions (e.g., neonatal alloimmune thrombocytopenia, neonatal autoimmune thrombocytopenia, fetal genetic syndromes, inborn errors of metabolism, anemia, apnea, bradycardia and other heart defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis (e.g., abdominal wall defects including omphalocele and others), hydrocephaly, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and fetal development stages or states (e.g., normal fetal organ function or development, and abnormal fetal organ function or development).

[0022]

[0019] In some embodiments, the pregnancy-related state is a subtype of preeclampsia and the at least three distinct pregnancy-related states include at least two distinct subtypes of preeclampsia, at least three distinct subtypes of preeclampsia, or at least four distinct subtypes of preeclampsia. In some embodiments, the subtype of preeclampsia is a molecular subtype of preeclampsia, and the at least two distinct subtypes of preeclampsia include at least two distinct molecular subtypes of preeclampsia, at least three distinct subtypes of preeclampsia, or at least four distinct subtypes of preeclampsia. In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of history of chronic or pre-existing hypertension, presence or history of gestational hypertension, presence or history of mild preeclampsia, presence or history of severe preeclampsia, presence or history of eclampsia, presence or history of preeclampsia with severe features, and presence or history of hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome. In some embodiments, the molecular subtype of preeclampsia is pre-term preeclampsia or term preeclampsia with severe features, and the set of biomarkers comprises a genomic locus associated with pre-term preeclampsia or term preeclampsia with severe features. In some embodiments, the genomic locus associated with pre-term preeclampsia or term preeclampsia with severe features is selected from the group consisting of genes listed in Table 1.

[0020] In some embodiments, the genomic locus associated with molecular subtype of PAPPA2+ preterm preeclampsia associated with mostly driven by PAPPA2 gene is selected from the group consisting of genes listed in Table 4.

[0023]

[0021] In some embodiments, the pre-term preeclampsia comprises delivery at less than 25 weeks, delivery at less than 26 weeks, delivery at less than 27 weeks, delivery at less than 28 weeks, delivery at less than 29 weeks, delivery at less than 30 weeks, delivery at less than 31 weeks, delivery at less than 32 weeks, delivery at less than 33 weeks, delivery at less than 34 weeks, delivery at less than 35 weeks, delivery at less than 36 weeks, delivery at less than 37 weeks, or delivery at less than 38 weeks. In some embodiments, the method further comprises identifying a clinical intervention for the pregnant subject based at least in part on the presence or elevated risk of the molecular subtype of preeclampsia. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the clinical intervention comprises a drug, a supplement, or a lifestyle recommendation. In some embodiments, the drug is selected from the group consisting of aspirin, progesterone, magnesium sulfate, a cholesterol medication, a heartburn medication, an angiotensin II receptor antagonist, a calcium channel blocker, a diabetes medication, and an erectile dysfunction medication. In some embodiments, the drug is selected from the group consisting of PAPP A2 modulating compounds comprises siRNA, RNA interference nucleic based molecules, and transcription factor modulators.

[0024]

[0022] In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of term preeclampsia (e.g., pregnant subjects with preeclampsia who delivered after 37 weeks (>37 weeks)). In some embodiments, the molecular subtype of preeclampsia is term preeclampsia (>37 weeks), and the set of biomarkers comprises a genomic locus associated with term preeclampsia (>37 weeks). In some embodiments, the genomic locus associated with term preeclampsia (>37 weeks) is selected from the group consisting of genes listed in Table 2.

[0025]

[0023] In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of term preeclampsia (e.g., pregnant subjects with preeclampsia who delivered after 37 weeks (>37 weeks)) without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension. In some embodiments, the molecular subtype of preeclampsia selected from group consisting of term preeclampsia (>37 weeks), without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension and the set of biomarkers comprises a genomic locus associated with term preeclampsia (>37 weeks) without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension. In some embodiments, the genomic locus associated with term preeclampsia (>37 weeks) without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension is selected from the group consisting of genes listed in Table 6 and Table 7.

[0026]

[0024] In some embodiments, the term preeclampsia comprises delivery after 37 weeks, delivery after 38 weeks, delivery after 39 weeks, delivery after 40 weeks, delivery at less than 29 weeks, delivery at less than 30 weeks, delivery at less than 31 weeks, delivery after 41 weeks, delivery after 42 weeks, or delivery after 43 weeks. In some embodiments, the method further comprises identifying a clinical intervention for the pregnant subject based at least in part on the presence or elevated risk of the molecular subtype of preeclampsia. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the clinical intervention comprises a drug, a supplement, or a lifestyle recommendation. In some embodiments, the drug is selected from the group consisting of aspirin, progesterone, magnesium sulfate, a cholesterol medication, a heartburn medication, an angiotensin II receptor antagonist, a calcium channel blocker, a diabetes medication, and an erectile dysfunction medication. In some embodiments, the drug is selected from the group consisting of CD 163 mRNA modulating compounds comprises siRNA, RNA interference nucleic based molecules, and transcription factor modulators.

[0027]

[0025] In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of postpartum preeclampsia (e.g., pregnant subjects who developed preeclampsia within 6 weeks after birth).

[0028]

[0026] In some embodiments, biological samples are assayed. For example, the assaying may comprise using cell-free ribonucleic acid (cfRNA) molecules derived from the cell-free biological sample to generate transcriptomic data, using transcription products derived from the cell-free biological sample to generate transcription product data, using cell-free deoxyribonucleic acid (cfDNA) molecules derived from the cell-free biological sample to generate genomic data and / or methylation data, using proteins derived from the first cell-free biological sample to generate proteomic data, or using metabolites derived from the first cell- free biological sample to generate metabolomic data.

[0029]

[0027] In some embodiments, the cell-free biological sample is selected from the group consisting of cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell- free fetal DNA (cffDNA), plasma, serum, urine, saliva, amniotic fluid, and derivatives thereof. In some embodiments, the cell-free biological sample is obtained or derived from the pregnant subject using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube, or a cell-free deoxyribonucleic acid (DNA) collection tube. In some embodiments, the method further comprises fractionating a whole blood sample of the pregnant subject to obtain the cell-free biological sample. In some embodiments, the assaying comprises a cell-free ribonucleic acid (cfRNA) assay or a metabolomics assay. In some embodiments, the metabolomics assay comprises targeted mass spectroscopy (MS) or an immune assay. In some embodiments, the cell-free biological sample comprises cell-free ribonucleic acid (cfRNA) or urine. In some embodiments, the assaying comprises quantitative polymerase chain reaction (qPCR). In some embodiments, the assaying comprises a home use test configured to be performed in a home setting.

[0030]

[0028] In some embodiments, a trained algorithm is used to determine the presence or elevated risk of the pregnancy-related state of the pregnant subject at a sensitivity of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. In some embodiments, the trained algorithm determines the presence or elevated risk of the pregnancy-related state of the pregnant subject at a positive predictive value (PPV) of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. In some embodiments, the trained algorithm determines the presence or elevated risk of the pregnancy-related state of the pregnant subject with an Area Under Curve (AUC) of at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, or at least about 0.95.

[0031]

[0029] In some embodiments, the pregnant subject is asymptomatic for the pregnancy-related state.

[0032]

[0030] In some embodiments, the trained algorithm is trained using a first set of independent training samples associated with a presence or elevated risk of the pregnancy -related state and a second set of independent training samples associated with an absence or no elevated risk of the pregnancy -related state.

[0033]

[0031] In some embodiments, the method further comprises using the trained algorithm or another trained algorithm to process a set of clinical health data of the pregnant subject to determine the presence or elevated risk of the pregnancy-related state.

[0034]

[0032] In some embodiments, the method further comprises subjecting the cell-free biological sample to conditions that are sufficient to isolate, enrich, or extract a set of ribonucleic (RNA) molecules, deoxyribonucleic acid (DNA) molecules, proteins, or metabolites; and wherein the assaying comprises analyzing the set of RNA molecules, DNA molecules, proteins, or metabolites. In some embodiments, the method further comprises extracting a set of nucleic acid molecules from the cell-free biological sample, and subjecting the set of nucleic acid molecules to sequencing to generate a set of sequencing reads. In some embodiments, the sequencing comprises massively parallel sequencing. In some embodiments, the sequencing comprises nucleic acid amplification. In some embodiments, the nucleic acid amplification comprises polymerase chain reaction (PCR). In some embodiments, the PCR comprises digital PCR. In some embodiments, the digital PCR comprises digital droplet PCR. In some embodiments, the sequencing comprises RNA sequencing. In some embodiments, the RNA sequencing comprises single-molecule RNA sequencing. In some embodiments, the sequencing comprises use of reverse transcription (RT) and polymerase chain reaction (PCR). In some embodiments, the method further comprises using probes configured to selectively enrich the set of nucleic acid molecules corresponding to a panel of one or more genomic loci. In some embodiments, the probes are nucleic acid primers. In some embodiments, the probes have sequence complementarity with nucleic acid sequences of the panel of the one or more genomic loci.

[0035]

[0033] In some embodiments, the cell-free biological sample is processed without nucleic acid isolation, enrichment, or extraction.

[0036]

[0034] In some embodiments, the method further comprises generating an electronic report comprising an indication of the determined presence or elevated risk of the pregnancy -related state.

[0037]

[0035] In some embodiments, the method further comprises determining a likelihood of the determination of the presence or elevated risk of the pregnancy-related state of the pregnant subject.

[0038]

[0036] In some embodiments, the trained algorithm comprises a trained machine learning algorithm. In some embodiments, the trained machine learning algorithm comprises a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a linear regression model, a logistic regression model, or an ANOVA model.

[0039]

[0037] In some embodiments, the method further comprises processing the set of biomarkers to reduce systematic variations. In some embodiments, reducing the systematic variations comprises using residuals from multivariate linear regression to correct data residuals, performing a ComBat method based on an empirical Bayes approach, and performing a surrogate variables analysis (SVA) correction. In some embodiments, the systematic variations comprise a depth of sequencing per sample, batch effects for individual process operations, use of various raw materials, local outside temperature of sample collection, BMI of the subject, fetal fraction, fetal gestational age at sample collection, or a combination thereof.

[0040]

[0038] In some embodiments, the method further comprises monitoring the presence or elevated risk of the pregnancy-related state, wherein the monitoring comprises assessing the presence or elevated risk of the pregnancy-related state of the pregnant subject at a plurality of time points, wherein the assessing is based at least on the presence or elevated risk of the pregnancy-related state determined at each of the plurality of time points. In some embodiments, a difference in the assessment of the presence or elevated risk of the pregnancy-related state of the pregnant subject among the plurality of time points is indicative of one or more clinical indications selected from the group consisting of: (i) a diagnosis of the presence or elevated risk of the pregnancy-related state of the pregnant subject, (ii) a prognosis of the presence or elevated risk of the pregnancy- related state of the pregnant subject, and (iii) an efficacy or non-efficacy of a course of treatment for treating the presence or elevated risk of the pregnancy-related state of the pregnant subject.

[0041]

[0039] In some embodiments, the pregnant subject is in a first trimester of pregnancy, a second trimester of pregnancy, or a third trimester of pregnancy.

[0042]

[0040] In some embodiments, the reference value is determined from pregnant subjects and / or non-pregnant subjects. In some embodiments, processing the set of biomarkers against the reference value comprises determining a difference between the set of biomarkers and the reference value.

[0043]

[0041] In some embodiments, the pregnancy-related state comprises preeclampsia. In some embodiments, a therapeutic intervention for the preeclampsia comprises a drug, a supplement, remote patient monitoring or a lifestyle recommendation. In some embodiments, the drug is selected from the group consisting of aspirin, progesterone, magnesium sulfate, a cholesterol medication (such as pravastatin), a heartbum medication (such as esomeprazole), an angiotensin II receptor antagonist (such as losartan), a calcium channel blocker (such as nifedipine), a diabetes medication (such as myo-inositol, metformin, glyburide, and liraglutide), and an erectile dysfunction medication (such as sildenafil citrate). In some embodiments, the supplement is selected from the group consisting of calcium, vitamin D, vitamin B3, and DHA. In some embodiments, the lifestyle recommendation is selected from the group consisting of exercise, nutrition counseling, meditation, stress relief, weight loss or maintenance, improving sleep quality with sleep study, and prescription of continuous positive airway pressure for treatment of obstructive sleep apnea. In some embodiments, the therapeutic intervention for the preeclampsia is selected from a therapeutic intervention (e.g., treatment or prophylaxis) as disclosed in “WHO recommendations: Prevention and treatment of pre-eclampsia and eclampsia,” World Health Organization, ISBN 9789241548335, World Health Organization, 2011, which is incorporated by reference herein in its entirety. In some embodiments, the therapeutic intervention for the preeclampsia is selected from a therapeutic intervention (e.g., treatment or prophylaxis) as disclosed in “Summary of recommendations: Prevention and treatment of pre-eclampsia and eclampsia,” World Health Organization, WHO reference number WHO / RHR / 11.30, World Health Organization, 2011, which is incorporated by reference herein in its entirety. In some embodiments, the therapeutic intervention for the preeclampsia is selected from a therapeutic intervention (e.g., treatment or prophylaxis) as disclosed in “WHO recommendations: Drug treatment for severe hypertension in pregnancy,” World Health Organization, ISBN 9789241550437, World Health Organization, 2018, which is incorporated by reference herein in its entirety.

[0044]

[0042] In some embodiments, the therapeutic intervention is selected based on molecular subtype of preeclampsia related to early placentation steps responsible for modulating the vasodilatory mediators and inhibiting vascular remodeling, platelet aggregation, and platelet adhesion and comprising drug selected from group of direct-acting vasodilators (hydralazine, minoxidil, nitrates, nitroprusside); calcium channel blockers (verapamil, diltiazem, nifedipine, amlodipine); an antagonist of the renin-angiotensin-aldosterone system (angiotensin receptor blockers, angiotensin-converting-enzyme inhibitors); Beta-2 receptor agonist (salbutamol, terbutaline); ostsynaptic alpha- 1 receptor antagonist (prazosin, phenoxyb enzamine, phentolamine); centrally acting alpha-2 receptor agonist (clonidine, a-methyldopa); centrally acting alpha-2 receptor agonist (clonidine, a-methyldopa); Centrally acting alpha-2 receptor agonist (clonidine, a-methyldopa).

[0045]

[0043] In some embodiments, the therapeutic intervention is selected based on molecular subtype of preeclampsia related to the molecular subtype of PE associated with keratinocyte endothelium pathway and comprising drug selected from group of proton pump inhibitors (PPI): omeprazole, esomeprazole, pantoprazole, rabeprazole, or lansoprazole.

[0046]

[0044] In some embodiments, the method of targeted gene mRNA expression modulation further comprises modulation by using siRNA molecules and / or ASO molecules. In some embodiments siRNA sequences can be generated by computation algorithms. Used algorithms often include design features that included both the structural features of the targeted RNAs and the sequence features of the siRNAs substantially improved the efficacy of siRNAs.

[0047]

[0045] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation further comprises modulation by using siRNA molecules and / or ASO molecules. In some embodiments, the siRNA design and compositions that can be used for modulation of PAPPA2 mRNA molecules are selected from the group consisting of siRNA sequences listed in Table 5. In some embodiments, the siRNA design and compositions that can be used for modulation of CD 163 mRNA are selected from the group consisting of siRNA sequences listed in Table 8.

[0048]

[0046] In some embodiments, typical screening design of siRNA is of about 15 / 20 asymmetric duplex with alternating 2’0-methyl / 2’Fluoro base modifications and phosphorothioates on the ends. In some embodiments, siRNA molecules with additional modifications can be use containing sugar modifications (2-OMe, -F, -O-allyl, -amino, orthoesters and LNA analogues), intemucleotide phospodiester bond modifications (phosphorothioates, boranophosphates), base modifications (s2U) , and as 3-terminal cholesterol-conjugates.

[0047] In some embodiments, sense strand of siRNA can be between 15 to 30 bases and complementary sense can be between 15 to 30 bases. In some embodiments, siRNA can be come from group containing including synthetic siRNAs, short hairpin RNAs (shRNAs), long dsRNAs, endoribonuclease-prepared short interfering RNAs, and pro-siRNAs.

[0049]

[0048] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation to further comprises their level modulation by modulating corresponding microRNA (miRNA) of small non-coding RNA which binds to 3’-UTR of the mRNA of their target genes (PAPPA2 and CD 163) with particular complementary manner, and thus lead to the translational repression of the targeting (PAPPA2 and CD 163) genes.

[0050]

[0049] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation comprises either increase or decrease of mRNA level expression, their level modulation by modulating corresponding transcription factors or regulatory factors. PAPPA2 level of mRNA can be modulated by transcription factors binding to corresponding binding sites listed in Table 3.

[0051]

[0050] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation is achieved by using a pharmaceutical compound that alters the transcription or translation.

[0052]

[0051] In a related aspect, the level of soluble forms of PAPP A2 and CD 163 in plasma can be modulated using antibody based compounds. In some embodiments, antibody based compounds are polyclonal antibody, monoclonal antibody, antibody fragments, bi specific antibodies, and antibody derivatives (e.g., antibody-drug conjugates and immunocytokines).

[0053]

[0052] In a related aspect, the modulating compound can target the cells carrying the receptor CD 163. In some embodiments, the cells are limited to cells of the monocytic lineage, including circulating monocytes and macrophages.

[0054]

[0053] In some embodiments, the method of detecting subtype of preeclampsia further comprises subjecting the cell-free biological sample to conditions that are sufficient to isolate, enrich, or extract a set of ribonucleic (RNA) molecules, deoxyribonucleic acid (DNA) molecules, proteins, or metabolites; and wherein the assaying comprises analyzing the set of RNA molecules, DNA molecules, proteins, or metabolites. In some embodiments, the method further comprises extracting a set of nucleic acid molecules from the cell-free biological sample, and subjecting the set of nucleic acid molecules to sequencing to generate a set of sequencing reads. In some embodiments, the sequencing comprises massively parallel sequencing. In some embodiments, the sequencing comprises nucleic acid amplification. In some embodiments, the nucleic acid amplification comprises polymerase chain reaction (PCR). In some embodiments, the sequencing comprises use of reverse transcription (RT) and polymerase chain reaction (PCR). In some embodiments, the method further comprises using probes configured to selectively enrich the set of nucleic acid molecules corresponding to a panel of one or more genomic loci. In some embodiments, the probes are nucleic acid primers.

[0055]

[0054] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure.

[0056] Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

[0057] INCORPORATION BY REFERENCE

[0058]

[0055] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.

[0059] BRIEF DESCRIPTION OF THE DRAWINGS

[0060]

[0056] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:

[0061]

[0057] FIG. 1 illustrates a computer system that is programmed or otherwise configured to implement methods provided herein.

[0062]

[0058] FIG. 2 shows comparison of PAPP A2 expression differences across 8 different clinical groups. Each cell is colored by PAPPA2 effect size and an indication of statistical significance by multiple testing corrected Mann Whitney U. n.s., not significant, * p<0.05, ** p<0.01, *** p<0.001. ptPE, preeclampsia with delivery <37 weeks; termPE, preeclampsia with delivery >=37 weeks; severe, with severe features; not_severe, without severe features; diag_lt37, diagnosed before 37 weeks; diag_gte37, diagnosed at or after 37 weeks; exclpp / unk, excludes postpartum or unknown timing.

[0059] FIGs. 3A-3B show quantile-quantile plots for differential gene expression measured by Mann-Whitney U ranked statistics. FIG. 3A shows differential gene expression for PAPPA2+ PE samples compared to all other cohort training samples, FIG. 3B shows PAPPA2+ preeclampsia compared to PAPPA2- preeclampsia.

[0063]

[0060] FIG. 4 illustrates a schematic structure of siRNA molecule that is designed or otherwise configured to use with methods provided herein.

[0064]

[0061] FIGs. 5A-5B show quantile-quantile plots for differential gene expression after removing the upper quartile of PAPP A2 expression measured by Mann-Whitney U ranked statistics. FIG. 5A shows samples with diagnosis of preeclampsia against all others. FIG. 5B shows samples with gestational hypertension against all others.

[0065] DETAILED DESCRIPTION

[0066]

[0062] While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0067]

[0063] 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 “a nucleic acid” includes a plurality of nucleic acids, including mixtures thereof.

[0068]

[0064] As used herein, the term “subject,” generally refers to an entity or a medium that has testable or detectable genetic information. A subject can be a person, individual, or patient. A subject can be a vertebrate, such as, for example, a mammal. Non-limiting examples of mammals include humans, simians, farm animals, sport animals, rodents, and pets. A subject can be a pregnant female subject. The subject can be a woman having a fetus (or multiple fetuses) or suspected of having the fetus (or multiple fetuses). The subject can be a person that is pregnant or is suspected of being pregnant. The subject may be displaying a symptom(s) indicative of a health or physiological state or condition of the subject, such as a pregnancy -related health or physiological state or condition of the subject. As an alternative, the subject can be asymptomatic with respect to such health or physiological state or condition.

[0069]

[0065] The term “pregnancy-related state,” as used herein, generally refers to any health, physiological, and / or biochemical state or condition of a subject that is pregnant or is suspected of being pregnant, or of a fetus (or multiple fetuses) of the subject. Examples of pregnancy- related states include, without limitation, pre-term birth (e.g., spontaneous pre-term birth or medically-indicated pre-term birth), full-term birth, gestational age, due date (e.g., due date for an unborn baby or fetus of a subject), onset of labor, cholestasis, oligohydramnios, polyhydramnios, pregnancy -related hypertensive disorders (e.g., preeclampsia), eclampsia, HELLP syndrome, gestational diabetes, a congenital disorder of a fetus of the subject, ectopic pregnancy, spontaneous abortion, stillbirth, antepartum fetal demise, intrapartum fetal demise, intrauterine fetal demise, post-partum complications (e.g., post-partum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during delivery, premature rupture of membrane, premature rupture of membrane in pre-term birth, prelabor preterm rupture of membranes, placenta accreta spectrum disorders and placental abruption, placenta previa (placenta covering the cervix), intrauterine growth restriction, fetal growth restriction, small for gestational age (SGA), macrosomia (large fetus for gestational age), neonatal conditions (e.g., neonatal alloimmune thrombocytopenia, neonatal autoimmune thrombocytopenia, fetal genetic syndromes, inborn errors of metabolism, anemia, apnea, bradycardia and other heart defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis (e.g., abdominal wall defects including omphalocele and others), hydrocephaly, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and fetal development stages or states (e.g., normal fetal organ function or development, and abnormal fetal organ function or development). For example, the fetal development stages or states may be related to normal fetal organ function or development and / or abnormal fetal organ function or development for a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus. In some situations, the pregnancy-related state is not associated with the health or physiological state or condition of a fetus (or multiple fetuses) of the subject.

[0070]

[0066] As used herein, the term “sample,” generally refers to a biological sample obtained from or derived from one or more subjects. Biological samples may be cell-free biological samples or substantially cell-free biological samples, or may be processed or fractionated to produce cell- free biological samples. For example, cell-free biological samples may include cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell-free fetal DNA (cffDNA), plasma, serum, urine, saliva, amniotic fluid, and derivatives thereof. Cell-free biological samples may be obtained or derived from subjects using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube (e.g., Streck), or a cell-free DNA collection tube (e.g., Streck). Cell-free biological samples may be derived from whole blood samples by fractionation. Biological samples or derivatives thereof may contain cells. For example, a biological sample may be a blood sample or a derivative thereof (e.g., blood collected by a collection tube or blood drops), a vaginal sample (e.g., a vaginal swab), or a cervical sample (e.g., a cervical swab).

[0071]

[0067] As used herein, the term “nucleic acid” generally refers to a polymeric form of nucleotides of any length, either deoxyribonucleotides (dNTPs) or ribonucleotides (rNTPs), or analogs thereof. Nucleic acids may have any three-dimensional structure, and may perform any function, known or unknown. Non-limiting examples of nucleic acids include deoxyribonucleic (DNA), ribonucleic acid (RNA), coding or non-coding regions of a gene or gene fragment, loci (locus) defined from linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, short interfering RNA (siRNA), short-hairpin RNA (shRNA), micro-RNA (miRNA), ribozymes, cDNA, recombinant nucleic acids, branched nucleic acids, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes, and primers. A nucleic acid may comprise one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure may be made before or after assembly of the nucleic acid. The sequence of nucleotides of a nucleic acid may be interrupted by non-nucleotide components. A nucleic acid may be further modified after polymerization, such as by conjugation or binding with a reporter agent.

[0072]

[0068] As used herein, the term “target nucleic acid” generally refers to a nucleic acid molecule in a starting population of nucleic acid molecules having a nucleotide sequence whose presence, amount, and / or sequence, or changes in one or more of these, are desired to be determined. A target nucleic acid may be any type of nucleic acid, including DNA, RNA, and analogs thereof.

[0073]

[0069] As used herein, a “target ribonucleic acid (RNA)” generally refers to a target nucleic acid that is RNA. As used herein, a “target deoxyribonucleic acid (DNA)” generally refers to a target nucleic acid that is DNA.

[0074]

[0070] As used herein, the terms “amplifying” and “amplification” generally refer to increasing the size or quantity of a nucleic acid molecule. The nucleic acid molecule may be singlestranded or double-stranded. Amplification may include generating one or more copies or “amplified product” of the nucleic acid molecule. Amplification may be performed, for example, by extension (e.g., primer extension) or ligation. Amplification may include performing a primer extension reaction to generate a strand complementary to a single-stranded nucleic acid molecule, and in some cases generate one or more copies of the strand and / or the single-stranded nucleic acid molecule. The term “DNA amplification” generally refers to generating one or more copies of a DNA molecule or “amplified DNA product.” The term “reverse transcription amplification” generally refers to the generation of deoxyribonucleic acid (DNA) from a ribonucleic acid (RNA) template via the action of a reverse transcriptase.

[0075]

[0071] Every year, about 15 million pre-term births as well as 10 million cases of preeclampsia are reported globally, and over 300,000 women die of pregnancy related complications such as hemorrhage and hypertensive disorders like preeclampsia. Preeclampsia may affect as many as about 8% of pregnancies, and is a leading cause of maternal mortality and morbidity. Pregnancy-related complications such as preeclampsia are a leading cause of maternal death and premature birth for baby followed by complications later in life. Further, such pregnancy -related complications can cause negative health effects on maternal health.

[0076]

[0072] Currently there are no known cures for preeclampsia. Preeclampsia can vary in severity from mild to life threatening. A mild form of pre-eclampsia can be treated with bed rest and frequent monitoring. For moderate to severe cases, hospitalization is recommended and blood pressure medication or anticonvulsant medications to prevent seizures are prescribed. If the condition becomes life threatening to the mother or the baby the pregnancy is terminated and the baby is delivered pre-term.

[0077]

[0073] Clinical manifestations of hypertension and proteinuria that define preeclampsia probably represent the late stage of a disease that begins very early in pregnancy. There are multiple theories, and little agreement, about the ultimate cause of preeclampsia, and it is likely that many different initial insults converge on a common pathophysiology (or two common pathophysiologies, if considering early- and late-onset preeclampsia separately). However, what is clear is that all forms of the disease are characterized by a disruption of vascular remodeling and a systemic anti-angiogenic response. The underlying mechanisms contributing to these changes remain unclear and might overlap. Among the possible mechanisms that have been most studied are alterations in the maternal immune response to the allogenic fetus and placental oxygen dysregulation (including inappropriate placental hypoxia and hypoxia-reoxygenation injury).

[0078]

[0074] Morten Rasmussen at al. (“RNA profiles reveal signatures of future health and disease in pregnancy”, Nature, 601(7893), 422-427, January 5, 2022, which is incorporated by reference herein in its entirety) and Moufarrej et al. (“Early prediction of preeclampsia in pregnancy with cell-free RNA”, Nature, 602, 689-694, February 9, 2022, which is incorporated by reference herein in its entirety) showed that the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia. cfRNA profiling indicates at least two separate groups of genes and pathways are dysregulated at early stages of preeclampsia. Placental and kidney associated PAPPA2 gene expression is upregulated in the plasma of mothers at early stages of pregnancy and predictive of mothers devel oping a severe form of preeclampsia, and immune balance related CD 163 gene is predictive of mothers developing a mild form of preeclampsia.

[0079]

[0075] Pregnancy-associated plasma protein A2 (PAPPA2) is an insulin-like growth factorbinding protein (IGFBP) protease expressed at high levels in the placenta and upregulated in pregnancies complicated by preeclampsia and HELLP (Hemolytic anemia, Elevated Liver enzymes, and Low Platelet count) syndrome. However, it is unclear whether elevated PAPPA2 expression causes abnormal placental development, or whether upregulation compensates for placental pathology. In the present study, we investigate whether PAPPA2 expression is affected by hypoxia, oxidative stress, syncytialization factors or substances known to affect the expression of PAPPA2's paralogue, PAPPA (Wagner et al., “Regulation of pregnancy- associated plasma protein A2 (PAPPA2) in a human placental trophoblast cell line (BeWo)”, Reprod Biol Endocrinol, 2011 Apr 15:9:48, which is incorporated by reference herein in its entirety).

[0080]

[0076] PAPPA2 and genetic variations are associated with salt sensitivity, blood pressure changes and hypertension in human (Wang et al., “Associations of plasma PAPP-A2 and genetic variations with salt sensitivity, blood pressure changes and hypertension incidence in Chinese adults”, Journal of Hypertension, 39(9): 1817-1825, September 2021, which is incorporated by reference herein in its entirety).

[0081]

[0077] Preeclampsia (PE) is a pregnancy-specific syndrome characterized by a systemic inflammatory response that polarizes peripheral blood monocytes to the Ml phenotype. The classically activated Ml monocytes comprise immune effector cells with an acute inflammatory phenotype. CD 163 is a scavenger receptor expressed by monocytes / macrophages that may be shed from their cell membrane after proteolytic cleavage, producing the soluble CD 163 molecule (sCD163).

[0082]

[0078] Careful regulation of PAPP A2 and CD 163 signaling pathways is critical for maintaining appropriate proliferation, migration, and angiogenesis by trophoblast cells in the developing placenta and immune responses. There is a need for methods of accurately diagnosing subjects at risk for or having preeclampsia associated with different subtypes of pathway dysregulation. A specific treatment targeting specific pathways may be needed for better treatment.

[0083]

[0079] Currently, there may be a lack of meaningful, clinically actionable diagnostic screenings or tests available for many pregnancy-related complications such as pre-term birth, preeclampsia, and growth restriction of the fetus. Antenatal screening tools may fail to detect up to 70% of pregnancies with a growth restricted fetus, therefore failing to identify those at greatest risk for adverse outcomes, including fetal mortality.

[0080] Thus, to make pregnancy as safe as possible, there exists a need for rapid, accurate methods for identifying specific subtypes of preeclampsia and applying specific treatment according to subtype of preeclampsia prior to development of preeclampsia.

[0084]

[0081] Using cfRNA expression analysis, we have discovered that cfRNA levels of PAPP A2 and CD 163 are elevated in plasma from pregnant women who are at high risk of developing two distinctive subtypes of preeclampsia. Modulation of PAPP A2 and CD 163 gene expression or protein levels to normal level may also lead to reducing the risk of developing preeclampsia.

[0085]

[0082] Small interfering RNAs (siRNAs) may refer to 10-21 nt long or 21-23 nt long doublestranded oligoribonucleotides, which in mammalian cells exhibit a potency for sequence-specific gene silencing via an RNA interference (RNAi) pathway. SiRNA can be used to modulate the levels of genes, such as PAPPA2 and CD 163. Antisense oligonucleotides (ASOs) may refer to single-stranded, chemically modified DNA or RNA molecules that bind to complementary messenger RNA (mRNA) sequences, thereby blocking protein synthesis or triggering RNA degradation.

[0086]

[0083] ASOs and / or siRNAs may be used for therapeutic applications in various pregnancy- related conditions, such as preeclampsia. ASOs and siRNAs may be highly specific for targeted RNA modulation and gene silencing, can be designed to target one or multiple mRNA transcripts simultaneously, and may be simple to synthesize and chemically modify. The RNA modulation therapy may comprise suitable approaches for delivery to specific cells or tissues (e.g., placental tissue), minimizing off-target effects, and minimizing immune responses or toxicity.

[0087]

[0084] The present disclosure provides methods, systems, and kits for identifying or monitoring pregnancy-related states by processing cell-free biological samples obtained from or derived from subjects. Cell-free biological samples (e.g., plasma samples) obtained from subjects may be analyzed to identify the pregnancy-related state (which may include, e.g., measuring a presence, absence, or relative assessment of the pregnancy -related state). Such subjects may include subjects with one or more pregnancy -related states and subjects without pregnancy- related states. Pregnancy -related states may include, for example, pre-term birth, full-term birth, gestational age, due date (e.g., due date for an unborn baby or fetus of a subject), onset of labor, cholestasis, ligohydramnios, polyhydramnios, pregnancy-related hypertensive disorders (e.g., preeclampsia), eclampsia, HELLP syndrome, gestational diabetes, a congenital disorder of a fetus of the subject, ectopic pregnancy, spontaneous abortion, stillbirth, antepartum and intrapartum fetal demise, intrauterine fetal demise, post-partum complications (e.g., post-partum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during delivery, premature rupture of membrane, premature rupture of membrane in pre-term birth, prelabor preterm rupture of membranes, placenta accreta spectrum disorders and placental abruption, placenta previa (placenta covering the cervix), intrauterine growth restriction, fetal growth restriction, small for gestational age (SGA), macrosomia (large fetus for gestational age), neonatal conditions (e.g., neonatal alloimmune thrombocytopenia and neonatal autoimmune thrombocytopeni, fetal genetic syndromes, inborn errors of metabolism, anemia, apnea, bradycardia and other heart defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis (e.g., abdominal wall defects including omphalocele and others) hydrocephaly, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and fetal development stages or states (e.g., normal fetal organ function or development, and abnormal fetal organ function or development). For example, the fetal development stages or states may be related to normal fetal organ function or development and / or abnormal fetal organ function or development for a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus.

[0088]

[0085] In an aspect, the present disclosure provides a method for identifying a presence or elevated risk of a pregnancy-related state of a pregnant subject, comprising assaying a cell-free biological sample derived from the pregnant subject to detect a set of biomarkers, and processing the set of biomarkers with a trained algorithm or against a reference value to determine the presence or elevated risk of the pregnancy-related state among a set of at least three distinct pregnancy-related states.

[0089]

[0086] In some embodiments, the pregnancy-related state is selected from the group consisting of pre-term birth, full-term birth, gestational age, due date (e.g., due date for an unborn baby or fetus of a subject), onset of labor, cholestasis, oligohydramnios, polyhydramnios, pregnancy- related hypertensive disorders (e.g., preeclampsia), eclampsia, HELLP syndrome, gestational diabetes, a congenital disorder of a fetus of the subject, ectopic pregnancy, spontaneous abortion, stillbirth, antepartum fetal demise, intrapartum fetal demise, intrauterine fetal demise, post-partum complications (e.g., post-partum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during delivery, premature rupture of membrane, premature rupture of membrane in pre-term birth, prelabor preterm rupture of membranes, placenta accreta spectrum disorders and placental abruption, placenta previa (placenta covering the cervix), intrauterine growth restriction, fetal growth restriction, small for gestational age (SGA), macrosomia (large fetus for gestational age), neonatal conditions (e.g., neonatal alloimmune thrombocytopenia, neonatal autoimmune thrombocytopenia, fetal genetic syndromes, inborn errors of metabolism, anemia, apnea, bradycardia and other heart defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis (e.g., abdominal wall defects including omphalocele and others), hydrocephaly, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and fetal development stages or states (e.g., normal fetal organ function or development, and abnormal fetal organ function or development).

[0090]

[0087] In some embodiments, the pregnancy-related state is a subtype of preeclampsia and the at least three distinct pregnancy-related states include at least two distinct subtypes of preeclampsia, at least three distinct subtypes of preeclampsia, or at least four distinct subtypes of preeclampsia. In some embodiments, the subtype of preeclampsia is a molecular subtype of preeclampsia, and the at least two distinct subtypes of preeclampsia include at least two distinct molecular subtypes of preeclampsia, at least three distinct subtypes of preeclampsia, or at least four distinct subtypes of preeclampsia. In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of history of chronic or pre-existing hypertension, presence or history of gestational hypertension, presence or history of mild preeclampsia, presence or history of severe preeclampsia, presence or history of eclampsia, presence or history of preeclampsia with severe features, and presence or history of hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome. In some embodiments, the molecular subtype of preeclampsia is pre-term preeclampsia or term preeclampsia with severe features, and the set of biomarkers comprises a genomic locus associated with pre-term preeclampsia or term preeclampsia with severe features. In some embodiments, the genomic locus associated with pre-term preeclampsia or term preeclampsia with severe features is selected from the group consisting of genes listed in Table 1.

[0091]

[0088] Table 1: Set of 136 genes predictive of distinct subtypes of preterm preeclampsia and preeclampsia with severe features.

[0092]

[0089] In some embodiments, the genomic locus associated with molecular subtype of PAPPA2+ preterm preeclampsia associated with mostly driven by PAPPA2 gene is selected from the group consisting of genes listed in Table 4.

[0093]

[0090] In some embodiments, the pre-term preeclampsia comprises delivery at less than 25 weeks, delivery at less than 26 weeks, delivery at less than 27 weeks, delivery at less than 28 weeks, delivery at less than 29 weeks, delivery at less than 30 weeks, delivery at less than 31 weeks, delivery at less than 32 weeks, delivery at less than 33 weeks, delivery at less than 34 weeks, delivery at less than 35 weeks, delivery at less than 36 weeks, delivery at less than 37 weeks, or delivery at less than 38 weeks. In some embodiments, the method further comprises identifying a clinical intervention for the pregnant subject based at least in part on the presence or elevated risk of the molecular subtype of preeclampsia. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the clinical intervention comprises a drug, a supplement, or a lifestyle recommendation. In some embodiments, the drug is selected from the group consisting of aspirin, progesterone, magnesium sulfate, a cholesterol medication, a heartburn medication, an angiotensin II receptor antagonist, a calcium channel blocker, a diabetes medication, and an erectile dysfunction medication. In some embodiments, the drug is selected from the group consisting of PAPP A2 modulating compounds comprises siRNA, RNA interference nucleic based molecules, and transcription factor modulators.

[0094]

[0091] In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of term preeclampsia (e.g., pregnant subjects with preeclampsia who delivered after 37 weeks (>37 weeks)). In some embodiments, the molecular subtype of preeclampsia is term preeclampsia (>37 weeks), and the set of biomarkers comprises a genomic locus associated with term preeclampsia (>37 weeks). In some embodiments, the genomic locus associated with term preeclampsia (>37 weeks) is selected from the group consisting of genes listed in Table 2.

[0095]

[0092] Table 2. Set of 81 genes predictive of distinct subtypes of term preeclampsia

[0096]

[0093] In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of term preeclampsia (e.g., pregnant subjects with preeclampsia who delivered after 37 weeks (>37 weeks)) without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension. In some embodiments, the molecular subtype of preeclampsia selected from group consisting of term preeclampsia (>37 weeks), without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension and the set of biomarkers comprises a genomic locus associated with term preeclampsia (>37 weeks) without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension. In some embodiments, the genomic locus associated with term preeclampsia (>37 weeks) without severe features, postpartum preeclampsia, gestation hypertension, or chronic hypertension is selected from the group consisting of genes listed in Table 6 and Table 7.

[0097]

[0094] In some embodiments, the term preeclampsia comprises delivery after 37 weeks, delivery after 38 weeks, delivery after 39 weeks, delivery after 40 weeks, delivery at less than 29 weeks, delivery at less than 30 weeks, delivery at less than 31 weeks, delivery after 41 weeks, delivery after 42 weeks, or delivery after 43 weeks. In some embodiments, the method further comprises identifying a clinical intervention for the pregnant subject based at least in part on the presence or elevated risk of the molecular subtype of preeclampsia. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the clinical intervention comprises a drug, a supplement, or a lifestyle recommendation. In some embodiments, the drug is selected from the group consisting of aspirin, progesterone, magnesium sulfate, a cholesterol medication, a heartburn medication, an angiotensin II receptor antagonist, a calcium channel blocker, a diabetes medication, and an erectile dysfunction medication. In some embodiments, the drug is selected from the group consisting of CD 163 mRNA modulating compounds comprises siRNA, RNA interference nucleic based molecules, and transcription factor modulators.

[0098]

[0095] In some embodiments, the molecular subtype of preeclampsia is selected from the group consisting of postpartum preeclampsia (e.g., pregnant subjects who developed preeclampsia within 6 weeks after birth).

[0099]

[0096] In some embodiments, biological samples are assayed. For example, the assaying may comprise using cell-free ribonucleic acid (cfRNA) molecules derived from the cell-free biological sample to generate transcriptomic data, using transcription products derived from the cell-free biological sample to generate transcription product data, using cell-free deoxyribonucleic acid (cfDNA) molecules derived from the cell-free biological sample to generate genomic data and / or methylation data, using proteins derived from the first cell-free biological sample to generate proteomic data, or using metabolites derived from the first cell- free biological sample to generate metabolomic data.

[0100]

[0097] In some embodiments, the cell-free biological sample is selected from the group consisting of cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell- free fetal DNA (cffDNA), plasma, serum, urine, saliva, amniotic fluid, and derivatives thereof. In some embodiments, the cell-free biological sample is obtained or derived from the pregnant subject using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube, or a cell-free deoxyribonucleic acid (DNA) collection tube. In some embodiments, the method further comprises fractionating a whole blood sample of the pregnant subject to obtain the cell-free biological sample. In some embodiments, the assaying comprises a cell-free ribonucleic acid (cfRNA) assay or a metabolomics assay. In some embodiments, the metabolomics assay comprises targeted mass spectroscopy (MS) or an immune assay. In some embodiments, the cell-free biological sample comprises cell-free ribonucleic acid (cfRNA) or urine. In some embodiments, the assaying comprises quantitative polymerase chain reaction (qPCR). In some embodiments, the assaying comprises a home use test configured to be performed in a home setting.

[0101]

[0098] In some embodiments, a trained algorithm is used to determine the presence or elevated risk of the pregnancy-related state of the pregnant subject at a sensitivity of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. In some embodiments, the trained algorithm determines the presence or elevated risk of the pregnancy-related state of the pregnant subject at a positive predictive value (PPV) of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. In some embodiments, the trained algorithm determines the presence or elevated risk of the pregnancy-related state of the pregnant subject with an Area Under Curve (AUC) of at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, or at least about 0.95.

[0102]

[0099] In some embodiments, the pregnant subject is asymptomatic for the pregnancy-related state.

[0103]

[0100] In some embodiments, the trained algorithm is trained using a first set of independent training samples associated with a presence or elevated risk of the pregnancy -related state and a second set of independent training samples associated with an absence or no elevated risk of the pregnancy -related state.

[0104]

[0101] In some embodiments, the method further comprises using the trained algorithm or another trained algorithm to process a set of clinical health data of the pregnant subject to determine the presence or elevated risk of the pregnancy-related state.

[0105]

[0102] In some embodiments, the method further comprises subjecting the cell-free biological sample to conditions that are sufficient to isolate, enrich, or extract a set of ribonucleic (RNA) molecules, deoxyribonucleic acid (DNA) molecules, proteins, or metabolites; and wherein the assaying comprises analyzing the set of RNA molecules, DNA molecules, proteins, or metabolites. In some embodiments, the method further comprises extracting a set of nucleic acid molecules from the cell-free biological sample, and subjecting the set of nucleic acid molecules to sequencing to generate a set of sequencing reads. In some embodiments, the sequencing comprises massively parallel sequencing. In some embodiments, the sequencing comprises nucleic acid amplification. In some embodiments, the nucleic acid amplification comprises polymerase chain reaction (PCR). In some embodiments, the PCR comprises digital PCR. In some embodiments, the digital PCR comprises digital droplet PCR. In some embodiments, the sequencing comprises RNA sequencing. In some embodiments, the RNA sequencing comprises single-molecule RNA sequencing. In some embodiments, the sequencing comprises use of reverse transcription (RT) and polymerase chain reaction (PCR). In some embodiments, the method further comprises using probes configured to selectively enrich the set of nucleic acid molecules corresponding to a panel of one or more genomic loci. In some embodiments, the probes are nucleic acid primers. In some embodiments, the probes have sequence complementarity with nucleic acid sequences of the panel of the one or more genomic loci.

[0103] In some embodiments, the cell-free biological sample is processed without nucleic acid isolation, enrichment, or extraction.

[0106]

[0104] In some embodiments, the method further comprises generating an electronic report comprising an indication of the determined presence or elevated risk of the pregnancy -related state.

[0107]

[0105] In some embodiments, the method further comprises determining a likelihood of the determination of the presence or elevated risk of the pregnancy-related state of the pregnant subject.

[0108]

[0106] In some embodiments, the trained algorithm comprises a trained machine learning algorithm. In some embodiments, the trained machine learning algorithm comprises a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a linear regression model, a logistic regression model, or an ANOVA model.

[0109]

[0107] In some embodiments, the method further comprises processing the set of biomarkers to reduce systematic variations. In some embodiments, reducing the systematic variations comprises using residuals from multivariate linear regression to correct data residuals, performing a ComBat method based on an empirical Bayes approach, and performing a surrogate variables analysis (SVA) correction. In some embodiments, the systematic variations comprise a depth of sequencing per sample, batch effects for individual process operations, use of various raw materials, local outside temperature of sample collection, BMI of the subject, fetal fraction, fetal gestational age at sample collection, or a combination thereof.

[0110]

[0108] In some embodiments, the method further comprises monitoring the presence or elevated risk of the pregnancy-related state, wherein the monitoring comprises assessing the presence or elevated risk of the pregnancy-related state of the pregnant subject at a plurality of time points, wherein the assessing is based at least on the presence or elevated risk of the pregnancy-related state determined at each of the plurality of time points. In some embodiments, a difference in the assessment of the presence or elevated risk of the pregnancy-related state of the pregnant subject among the plurality of time points is indicative of one or more clinical indications selected from the group consisting of: (i) a diagnosis of the presence or elevated risk of the pregnancy-related state of the pregnant subject, (ii) a prognosis of the presence or elevated risk of the pregnancy- related state of the pregnant subject, and (iii) an efficacy or non-efficacy of a course of treatment for treating the presence or elevated risk of the pregnancy-related state of the pregnant subject.

[0111]

[0109] In some embodiments, the pregnant subject is in a first trimester of pregnancy, a second trimester of pregnancy, or a third trimester of pregnancy.

[0112] [HO] In some embodiments, the reference value is determined from pregnant subjects and / or non-pregnant subjects. In some embodiments, processing the set of biomarkers against the reference value comprises determining a difference between the set of biomarkers and the reference value.

[0113] [Hl] In some embodiments, the pregnancy-related state comprises preeclampsia. In some embodiments, a therapeutic intervention for the preeclampsia comprises a drug, a supplement, remote patient monitoring or a lifestyle recommendation. In some embodiments, the drug is selected from the group consisting of aspirin, progesterone, magnesium sulfate, a cholesterol medication (such as pravastatin), a heartbum medication (such as esomeprazole), an angiotensin II receptor antagonist (such as losartan), a calcium channel blocker (such as nifedipine), a diabetes medication (such as myo-inositol, metformin, glyburide, and liraglutide), and an erectile dysfunction medication (such as sildenafil citrate). In some embodiments, the supplement is selected from the group consisting of calcium, vitamin D, vitamin B3, and DHA. In some embodiments, the lifestyle recommendation is selected from the group consisting of exercise, nutrition counseling, meditation, stress relief, weight loss or maintenance, improving sleep quality with sleep study, and prescription of continuous positive airway pressure for treatment of obstructive sleep apnea. In some embodiments, the therapeutic intervention for the preeclampsia is selected from a therapeutic intervention (e.g., treatment or prophylaxis) as disclosed in “WHO recommendations: Prevention and treatment of pre-eclampsia and eclampsia,” World Health Organization, ISBN 9789241548335, World Health Organization, 2011, which is incorporated by reference herein in its entirety. In some embodiments, the therapeutic intervention for the preeclampsia is selected from a therapeutic intervention (e.g., treatment or prophylaxis) as disclosed in “Summary of recommendations: Prevention and treatment of pre-eclampsia and eclampsia,” World Health Organization, WHO reference number WHO / RHR / 11.30, World Health Organization, 2011, which is incorporated by reference herein in its entirety. In some embodiments, the therapeutic intervention for the preeclampsia is selected from a therapeutic intervention (e.g., treatment or prophylaxis) as disclosed in “WHO recommendations: Drug treatment for severe hypertension in pregnancy,” World Health Organization, ISBN 9789241550437, World Health Organization, 2018, which is incorporated by reference herein in its entirety.

[0114]

[0112] In some embodiments, the therapeutic intervention is selected based on molecular subtype of preeclampsia related to early placentation steps responsible for modulating the vasodilatory mediators and inhibiting vascular remodeling, platelet aggregation, and platelet adhesion and comprising drug selected from group of direct-acting vasodilators (hydralazine, minoxidil, nitrates, nitroprusside); calcium channel blockers (verapamil, diltiazem, nifedipine, amlodipine); an antagonist of the renin-angiotensin-aldosterone system (angiotensin receptor blockers, angiotensin-converting-enzyme inhibitors); Beta-2 receptor agonist (salbutamol, terbutaline); ostsynaptic alpha- 1 receptor antagonist (prazosin, phenoxyb enzamine, phentolamine); centrally acting alpha-2 receptor agonist (clonidine, a-methyldopa); centrally acting alpha-2 receptor agonist (clonidine, a-methyldopa); Centrally acting alpha-2 receptor agonist (clonidine, a-methyldopa).

[0115]

[0113] In some embodiments, the therapeutic intervention is selected based on molecular subtype of preeclampsia related to the molecular subtype of PE associated with keratinocyte endothelium pathway and comprising drug selected from group of proton pump inhibitors (PPI): omeprazole, esomeprazole, pantoprazole, rabeprazole, or lansoprazole.

[0116]

[0114] In some embodiments, the method of targeted gene mRNA expression modulation further comprises modulation by using siRNA molecules and / or ASO molecules. In some embodiments, siRNA sequences can be generated by computation algorithms. Used algorithms often include design features that included both the structural features of the targeted RNAs and the sequence features of the siRNAs substantially improved the efficacy of siRNAs.

[0117]

[0115] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation further comprises modulation by using siRNA molecules and / or ASO molecules. In some embodiments, the siRNA design and compositions that can be used for modulation of PAPPA2 mRNA molecules are selected from the group consisting of siRNA sequences listed in Table 5. In some embodiments, the siRNA design and compositions that can be used for modulation of CD 163 mRNA are selected from the group consisting of siRNA sequences listed in Table 8.

[0118]

[0116] In some embodiments, a screening design of siRNA is of about 15 / 20 asymmetric duplex with alternating 2’0-methyl / 2’Fluoro base modifications and phosphorothioates on the ends. In some embodiments, siRNA molecules with additional modifications can be used containing sugar modifications (2-OMe, -F, -O-allyl, -amino, orthoesters and LNA analogues), intemucleotide phospodiester bond modifications (phosphorothioates, boranophosphates), base modifications (s2U) , and as 3-terminal cholesterol-conjugates.

[0119]

[0117] In some embodiments, a sense strand of siRNA can be between 15 to 30 bases, and a complementary sense can be between 15 to 30 bases. In some embodiments, siRNA can include synthetic siRNAs, short hairpin RNAs (shRNAs), long dsRNAs, endoribonuclease-prepared short interfering RNAs, and / or pro-siRNAs.

[0120]

[0118] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation further comprises modulation by modulating corresponding microRNA (miRNA) of small non-coding RNA which binds to 3’-UTR of the mRNA of their target genes (PAPPA2 and CD 163) with a particular complementary manner, thus leading to the translational repression of the targeting (PAPPA2 and CD 163) genes.

[0119] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation comprises an increase or decrease of mRNA level expression, where the modulation occurs by modulating corresponding transcription factors or regulatory factors. For example, PAPPA2 level of mRNA can be modulated by transcription factors binding to corresponding binding sites listed in Table 3.

[0121]

[0120] Table 3: Transcription factors modulating expression level of CD163 and PAPPA2 mRNA levels

[0122]

[0123]

[0121] In some embodiments, the method of PAPP A2 and CD 163 mRNA expression modulation is achieved by using a pharmaceutical compound that alters the transcription or translation.

[0124]

[0122] In a related aspect, the level of soluble forms of PAPP A2 and CD 163 in plasma can be modulated using antibody-based compounds. In some embodiments, examples of antibodybased compounds include polyclonal antibody, monoclonal antibody, antibody fragments, bispecific antibodies, and antibody derivatives (e.g., antibody-drug conjugates and immunocytokines) .

[0125]

[0123] In a related aspect, the modulating compound can target the cells carrying the receptor CD 163. In some embodiments, the cells are limited to cells of the monocytic lineage, including circulating monocytes and macrophages.

[0126]

[0124] In some embodiments, the method of detecting subtype of preeclampsia further comprises subjecting the cell-free biological sample to conditions that are sufficient to isolate, enrich, or extract a set of ribonucleic (RNA) molecules, deoxyribonucleic acid (DNA) molecules, proteins, or metabolites; and wherein the assaying comprises analyzing the set of RNA molecules, DNA molecules, proteins, or metabolites. In some embodiments, the method further comprises extracting a set of nucleic acid molecules from the cell-free biological sample, and subjecting the set of nucleic acid molecules to sequencing to generate a set of sequencing reads. In some embodiments, the sequencing comprises massively parallel sequencing. In some embodiments, the sequencing comprises nucleic acid amplification. In some embodiments, the nucleic acid amplification comprises polymerase chain reaction (PCR). In some embodiments, the sequencing comprises use of reverse transcription (RT) and polymerase chain reaction (PCR). In some embodiments, the method further comprises using probes configured to selectively enrich the set of nucleic acid molecules corresponding to a panel of one or more genomic loci. In some embodiments, the probes are nucleic acid primers.

[0127]

[0125] Computer systems

[0128]

[0126] The present disclosure provides computer systems that are programmed to implement methods of the disclosure. FIG. 1 shows a computer system 101 that is programmed or otherwise configured to, for example, (i) train and test a trained algorithm, (ii) use the trained algorithm to process data to determine a pregnancy -related state of a subject, (iii) determine a quantitative measure indicative of a pregnancy-related state of a subject, (iv) identify or monitor the pregnancy-related state of the subject, (v) electronically output a report that indicative of the pregnancy -related state of the subject, and (vi) design sequences of siRNA molecules or ASO molecules.

[0129]

[0127] The computer system 101 can regulate various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, (i) training and testing a trained algorithm, (ii) using the trained algorithm to process data to determine a pregnancy -related state of a subject, (iii) determining a quantitative measure indicative of a pregnancy-related state of a subject, (iv) identifying or monitoring the pregnancy -related state of the subject, (v) electronically outputting a report that indicative of the pregnancy-related state of the subject, and (vi) designing sequences of siRNA molecules or ASO molecules. The computer system 101 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[0130]

[0128] The computer system 101 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 105, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 101 also includes memory or memory location 110 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 115 (e.g., hard disk), communication interface 120 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 125, such as cache, other memory, data storage and / or electronic display adapters. The memory 110, storage unit 115, interface 120 and peripheral devices 125 are in communication with the CPU 105 through a communication bus (solid lines), such as a motherboard. The storage unit 115 can be a data storage unit (or data repository) for storing data. The computer system 101 can be operatively coupled to a computer network (“network”) 130 with the aid of the communication interface 120. The network 130 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet.

[0129] The network 130 in some cases is a telecommunication and / or data network. The network 130 can include one or more computer servers, which can enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing over the network 130 (“the cloud”) to perform various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, (i) training and testing a trained algorithm, (ii) using the trained algorithm to process data to determine a pregnancy-related state of a subject, (iii) determining a quantitative measure indicative of a pregnancy -related state of a subject, (iv) identifying or monitoring the pregnancy-related state of the subject, and (v) electronically outputting a report that indicative of the pregnancy-related state of the subject. Such cloud computing may be provided by cloud computing platforms such as, for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM cloud. The network 130, in some cases with the aid of the computer system 101, can implement a peer-to-peer network, which may enable devices coupled to the computer system 101 to behave as a client or a server.

[0131]

[0130] The CPU 105 may comprise one or more computer processors and / or one or more graphics processing units (GPUs). The CPU 105 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 110. The instructions can be directed to the CPU 105, which can subsequently program or otherwise configure the CPU 105 to implement methods of the present disclosure. Examples of operations performed by the CPU 105 can include fetch, decode, execute, and writeback.

[0132]

[0131] The CPU 105 can be part of a circuit, such as an integrated circuit. One or more other components of the system 101 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0133]

[0132] The storage unit 115 can store files, such as drivers, libraries and saved programs. The storage unit 115 can store user data, e.g., user preferences and user programs. The computer system 101 in some cases can include one or more additional data storage units that are external to the computer system 101, such as located on a remote server that is in communication with the computer system 101 through an intranet or the Internet.

[0134]

[0133] The computer system 101 can communicate with one or more remote computer systems through the network 130. For instance, the computer system 101 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 101 via the network 130.

[0134] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 101, such as, for example, on the memory 110 or electronic storage unit 115. The machine executable or machine readable code can be provided in the form of software. During use, the code can be executed by the processor 105. In some cases, the code can be retrieved from the storage unit 115 and stored on the memory 110 for ready access by the processor 105. In some situations, the electronic storage unit 115 can be precluded, and machine-executable instructions are stored on memory 110.

[0135]

[0135] The code can be pre-compiled and configured for use with a machine having a processer adapted to execute the code, or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0136]

[0136] Aspects of the systems and methods provided herein, such as the computer system 101, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk.

[0137]

[0137] “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non- transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0138]

[0138] Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0139]

[0139] The computer system 101 can include or be in communication with an electronic display 135 that comprises a user interface (UI) 140 for providing, for example, (i) a visual display indicative of training and testing of a trained algorithm, (ii) a visual display of data indicative of a pregnancy -related state of a subject, (iii) a quantitative measure of a pregnancy-related state of a subject, (iv) an identification of a subject as having a pregnancy -related state, (v) an electronic report indicative of the pregnancy-related state of the subject, or (vi) sequences of siRNA molecules or ASO molecules. Examples of UIs include, without limitation, a graphical user interface (GUI) and web-based user interface.

[0140]

[0140] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 205. The algorithm can, for example, (i) train and test a trained algorithm, (ii) use the trained algorithm to process data to determine a pregnancy-related state of a subject, (iii) determine a quantitative measure indicative of a pregnancy-related state of a subject, (iv) identify or monitor the pregnancy -related state of the subject, (v) electronically output a report that indicative of the pregnancy -related state of the subject, and (vi) design sequences of siRNA molecules or ASO molecules.

[0141] EXAMPLES

[0141] Example 1: Early prediction of preeclampsia using PAPPA2 driven molecular subtyping of preeclampsia and specific treatment by modulating PAPPA2 level mRNA by siRNA

[0142]

[0142] Using systems and methods of the present disclosure, early molecular markers for specific subtype of preeclampsia (PE) are identified in maternal blood samples from a population at increased risk of preeclampsia (PE) and treatment by modulating level of PAPP A2 mRNA by siRNA.

[0143]

[0143] Preterm preeclampsia is driven by PAPPA2 and a molecular subtype of preeclampsia can be defined based on gene expression profile. PAPPA2 is the most differentially expressed gene in a prospective cohort of 5,399 individuals. Overlaying PAPPA2 expression with a split of the data along 3 dimensions - diagnosis time before or after 37 weeks, delivery time before or after 37 weeks, and presence or absence of severe features - shows PAPPA2 signal clustering with preterm preeclampsia diagnosed before 37 weeks, delivered before 37 weeks and with severe features if diagnosed before 37 weeks (FIG. 2), we call this group PAPPA2+ preeclampsia.

[0144]

[0144] Within the PAPPA2+ preeclampsia several genes are differentially expressed as shown in FIG. 3A. Table 4 shows the top 20 differentially expressed genes in PAPPA2+ samples, highlighting raw and multiple testing p-values by Mann-Whitney U and effect size measured by cohen’s d. We also show that within individuals diagnosed with preeclampsia PAPPA2 is the only gene that is differentially expressed between PAPPA2+ and PAPPA2- preeclampsia (FIG.

[0145] 3B)

[0146]

[0145] Table 4: set of top 20 differentially expressed genes in PAPPA2+ samples.

[0147]

[0146] To modulate mRNA level of PAPP A2, a transcript sequence NM_020318.3 was used to design a set of candidate siRNA molecules.

[0148]

[0147] NM 020318.3 sequence

[0149]

[0148] 5 ’ AUGACUCUCUCUCUUGAGUAGGCAC AC ACUCCCUUUUCUCGGGUGUGUA CUUUUUGCUUUGUGAUACAUCUCUGCACUUUCAGUAUUUUCCAACUCAUCCUUA

[0150] AAUUCCUUCUCACAACAGUGUCAAGAGCCUGGACGCCAGCCAGGAUUGAGGUCC

[0151] UACGGGUGUUUGGGGACCUCCCCAAGCCCACGAGUAUCAAUGGCAGUAUCAAUU GUCUGUGACAGUGAUUAAGGAGCAAAACACUUGGAACCCACAAGACUCCCAGAA

[0152] GGUGAAGUUAAGAGCUCCCAGACUCAUAAGGUUAUUAGAACAGCAAACUGGCAC CCCAAAGAACUUUACGGAGACUUGCAACCUAUCAACAAGUUGGAUGAGGGAUUA

[0153] AAAGCCUUCAACAACCAACAACCCCAAGCAUCAAACUGAAGGAAACAUUCUAACC

[0154] UUCACAGACAGACUGGAGGCUGGAUGGGGACCUGGCUGAAGACAUCUGGAGAAU GAAAGUUAAGUACCAGCUUGCAUUUUUGUGCCCCUAGAUUAUUUUUGCAUUUUA

[0155] AAAUAAGAAGCAUCAAAUUGCGUGUCUCUGUGUAAAAGUUCUAGCAAUUUGUUU UAAGGUGAACUUAUUUUGGCUUAGGGACUACAAAAAGAGAAGGUAAUUCCUAGG GAAGGAAGAAGAGAAAGAAAUGAAAAUUAGAGAAUAAGAUUAUUUUGAAUGAC

[0156] UUCAGGUAGCGAGGAGUGUGUGUUUGUGAGUGUGUAUUUGAGAGACUUGGCUCA UGCCUGUGGGUCUUCUCUUCUAGUAUCAGUGAGGGGAGGGAUUACUGAAGAAGA

[0157] AGGGGGGAAAAAAAAAGAAAGAAAUCUGAGCUUUCUGGGAGGAAAUUCAAAGGA ACCAAGAGAAAUUAACUUCGUUCUGCAAGGACUAAAGUACAGCAAGAGGAGAGA GGUCAAGCGAGAAGCGUGCGGGAAGCACAUGCCCUGGGGAGGCAUAGAAGCCAC ACUGGCAGAGCGGCCAGCACAGGUAGCCAGCAGAGGCAUUCUUGGGGCUAUUUG AAAAAGUUUGGUCUGUGAACAAAACAGUUUCCCUGGUGACUGCAAAUCCAUUGC UAGCUGCCUCUUUCUCGUCUGCCCAUCACUCUGGUGUGGUACCCAGAAGUUGACU UCUGGUUCUGUAGAAAGAGCUAGGGGAGGUAUGAUGUGCUUAAAGAUCCUAAGA AUAAGCCUGGCGAUUUUGGCUGGGUGGGCACUCUGUUCUGCCAACUCUGAGCUG GGCUGGACACGCAAGAAAUCCUUGGUUGAGAGGGAACACCUGAAUCAGGUGCUG UUGGAAGGAGAACGUUGUUGGCUGGGGGCCAAGGUUCGAAGACCCAGAGCUUCU CCACAGCAUCACCUCUUUGGAGUCUACCCCAGCAGGGCUGGGAACUACCUAAGGC CCUACCCCGUGGGGGAGCAAGAAAUCCAUCAUACAGGACGCAGCAAACCAGACAC UGAAGGAAAUGCUGUGAGCCUUGUUCCCCCAGACCUGACUGAAAAUCCAGCAGG

[0158] ACUGAGGGGUGCAGUUGAAGAGCCGGCUGCCCCAUGGGUAGGGGAUAGUCCUAU

[0159] UGGGCAAUCUGAGCUGCUGGGAGAUGAUGACGCUUAUCUCGGCAAUCAAAGAUC

[0160] CAAGGAGUCUCUAGGUGAGGCCGGGAUUCAGAAAGGCUCAGCCAUGGCUGCCAC

[0161] UACUACCACCGCCAUUUUCACAACCCUGAACGAACCCAAACCAGAGACCCAAAGG

[0162] AGGGGCUGGGCCAAGUCCAGGCAGCGUCGCCAAGUGUGGAAGAGGCGGGCGGAA

[0163] GAUGGGCAGGGAGACUCCGGUAUCUCUUCACAUUUCCAACCUUGGCCCAAGCAU

[0164] UCCCUUAAACACAGGGUCAAAAAGAGUCCACCGGAGGAAAGCAACCAAAAUGGU

[0165] GGAGAGGGCUCCUACCGAGAAGCAGAGACCUUUAACUCCCAAGUAGGACUGCCC

[0166] AUCUUAUACUUCUCUGGGAGGCGGGAGCGGCUGCUGCUGCGUCCAGAAGUGCUG

[0167] GCUGAGAUUCCCCGGGAGGCGUUCACAGUGGAAGCCUGGGUUAAACCGGAGGGA

[0168] GGACAGAACAACCCAGCCAUCAUCGCAGGUGUGUUUGAUAACUGCUCCCACACUG

[0169] UCAGUGACAAAGGCUGGGCCCUGGGGAUCCGCUCAGGGAAGGACAAGGGAAAGC

[0170] GGGAUGCUCGCUUCUUCUUCUCCCUCUGCACCGACCGCGUGAAGAAAGCCACCAU

[0171] CUUGAUUAGCCACAGUCGCUACCAACCAGGCACAUGGACCCAUGUGGCAGCCACU

[0172] UACGAUGGACGGCACAUGGCCCUGUAUGUGGAUGGCACUCAGGUGGCUAGCAGU

[0173] CUAGACCAGUCUGGUCCCCUGAACAGCCCCUUCAUGGCAUCUUGCCGCUCUUUGC

[0174] UCCUGGGGGGAGACAGCUCUGAGGAUGGGCACUAUUUCCGUGGACACCUGGGCA

[0175] CACUGGUUUUCUGGUCGACCGCCCUGCCACAAAGCCAUUUUCAGCACAGUUCUCA

[0176] GCAUUCAAGUGGGGAGGAGGAAGCGACUGACUUGGUCCUGACAGCGAGCUUUGA

[0177] GCCUGUGAACACAGAGUGGGUUCCCUUUAGAGAUGAGAAGUACCCACGACUUGA

[0178] GGUUCUCCAGGGCUUUGAGCCAGAGCCUGAGAUUCUGUCGCCUUUGCAGCCCCCA

[0179] CUCUGUGGGCAAACAGUCUGUGACAAUGUGGAAUUGAUCUCCCAGUACAAUGGA

[0180] UACUGGCCCCUUCGGGGAGAGAAGGUGAUACGCUACCAGGUGGUGAACAUCUGU

[0181] GAUGAUGAGGGCCUAAACCCCAUUGUGAGUGAGGAGCAGAUUCGUCUGCAGCAC

[0182] GAGGCACUGAAUGAGGCCUUCAGCCGCUACAACAUCAGCUGGCAGCUGAGCGUCC

[0183] ACCAGGUCCACAAUUCCACCCUGCGACACCGGGUUGUGCUUGUGAACUGUGAGCC

[0184] CAGCAAGAUUGGCAAUGACCAUUGUGACCCCGAGUGUGAGCACCCACUCACAGGC

[0185] UAUGAUGGGGGUGACUGCCGCCUGCAGGGCCGCUGCUACUCCUGGAACCGCAGG

[0186] GAUGGGCUCUGUCACGUGGAGUGUAACAACAUGCUGAACGACUUUGACGACGGA

[0187] GACUGCUGCGACCCCCAGGUGGCUGAUGUGCGCAAGACCUGCUUUGACCCUGACU

[0188] CACCCAAGAGGGCAUACAUGAGUGUGAAGGAGCUGAAGGAGGCCCUGCAGCUGA

[0189] ACAGUACUCACUUCCUCAACAUCUACUUUGCCAGCUCAGUGCGGGAAGACCUUGC

[0190] AGGUGCUGCCACCUGGCCUUGGGACAAGGACGCUGUCACUCACCUGGGUGGCAU

[0191] UGUCCUCAGCCCAGCAUAUUAUGGGAUGCCUGGCCACACCGACACCAUGAUCCAU GAAGUGGGACAUGUUCUGGGACUCUACCAUGUCUUUAAAGGAGUCAGUGAAAGA

[0192] GAAUCCUGCAAUGACCCCUGCAAGGAGACAGUGCCAUCCAUGGAAACGGGAGAC

[0193] CUCUGUGCCGACACCGCCCCCACUCCCAAGAGUGAGCUGUGCCGGGAACCAGAGC

[0194] CCACUAGUGACACCUGUGGCUUCACUCGCUUCCCAGGGGCUCCGUUCACCAACUA

[0195] CAUGAGCUACACGGAUGAUAACUGCACUGACAACUUCACUCCUAACCAAGUGGCC

[0196] CGAAUGCAUUGCUAUUUGGACCUAGUCUAUCAGCAGUGGACUGAAAGCAGAAAG

[0197] CCCACCCCCAUCCCCAUUCCACCUAUGGUCAUCGGACAGACCAACAAGUCCCUCA

[0198] CUAUCCACUGGCUGCCUCCUAUUAGUGGAGUUGUAUAUGACAGGGCCUCAGGCA

[0199] GCUUGUGUGGCGCUUGCACUGAAGAUGGGACCUUUCGUCAGUAUGUGCACACAG

[0200] CUUCCUCCCGGCGGGUGUGUGACUCCUCAGGUUAUUGGACCCCAGAGGAGGCUG

[0201] UGGGGCCUCCUGAUGUGGAUCAGCCCUGCGAGCCAAGCUUACAGGCCUGGAGCCC

[0202] UGAGGUCCACCUGUACCACAUGAACAUGACGGUCCCCUGCCCCACAGAAGGCUGU

[0203] AGCUUGGAGCUGCUCUUCCAACACCCGGUCCAAGCCGACACCCUCACCCUGUGGG

[0204] UCACUUCCUUCUUCAUGGAGUCCUCGCAGGUCCUCUUUGACACAGAGAUCUUGCU

[0205] GGAAAACAAGGAGUCAGUGCACCUGGGCCCCUUAGACACUUUCUGUGACAUCCC

[0206] ACUCACCAUCAAACUGCACGUGGAUGGGAAGGUGUCGGGGGUGAAAGUCUACAC

[0207] CUUUGAUGAGAGGAUAGAGAUUGAUGCAGCACUCCUGACUUCUCAGCCCCACAG

[0208] UCCCUUGUGCUCUGGCUGCAGGCCUGUGAGGUACCAGGUUCUCCGCGAUCCCCCA

[0209] UUUGCCAGUGGUUUGCCCGUGGUGGUGACACAUUCUCACAGGAAGUUCACGGAC

[0210] GUGGAGGUCACACCUGGACAGAUGUAUCAGUACCAAGUUCUAGCUGAAGCUGGA

[0211] GGAGAACUGGGAGAAGCUUCGCCUCCUCUGAACCACAUUCAUGGAGCUCCUUAU

[0212] UGUGGAGAUGGGAAGGUGUCAGAGAGACUGGGAGAAGAGUGUGAUGAUGGAGA

[0213] CCUUGUGAGCGGAGAUGGCUGCUCCAAGGUGUGUGAGCUGGAGGAAGGUUUCAA

[0214] CUGUGUAGGAGAGCCAAGCCUUUGCUACAUGUAUGAGGGAGAUGGCAUAUGUGA

[0215] ACCUUUUGAGAGAAAAACCAGCAUUGUAGACUGUGGCAUCUACACUCCCAAAGG

[0216] AUACUUGGAUCAAUGGGCUACCCGGGCUUACUCCUCUCAUGAAGACAAGAAGAA

[0217] GUGUCCUGUUUCCUUGGUAACUGGAGAACCUCAUUCCCUAAUUUGCACAUCAUA

[0218] CCAUCCAGAUUUACCCAACCACCGUCCCCUAACUGGCUGGUUUCCCUGUGUUGCC

[0219] AGUGAAAAUGAAACUCAGGAUGACAGGAGUGAACAGCCAGAAGGUAGCCUGAAG

[0220] AAAGAGGAUGAGGUUUGGCUCAAAGUGUGUUUCAAUAGACCAGGAGAGGCCAGA

[0221] GCAAUUUUUAUUUUUUUGACAACUGAUGGCCUAGUUCCCGGAGAGCAUCAGCAG

[0222] CCGACAGUGACUCUCUACCUGACCGAUGUCCGUGGAAGCAACCACUCUCUUGGAA

[0223] CCUAUGGACUGUCAUGCCAGCAUAAUCCACUGAUUAUCAAUGUGACCCAUCACCA

[0224] GAAUGUCCUUUUCCACCAUACCACCUCAGUGCUGCUGAAUUUCUCAUCCCCACGG

[0225] GUCGGCAUCUCAGCUGUGGCUCUAAGGACAUCCUCCCGCAUUGGUCUUUCGGCUC CCAGUAACUGCAUCUCAGAGGACGAGGGGCAGAAUCAUCAGGGACAGAGCUGUA

[0226] UCCAUCGGCCCUGUGGGAAGCAGGACAGCUGUCCGUCAUUGCUGCUUGAUCAUG

[0227] CUGAUGUGGUGAACUGUACCUCUAUAGGCCCAGGUCUCAUGAAGUGUGCUAUCA

[0228] CUUGUCAAAGGGGAUUUGCCCUUCAGGCCAGCAGUGGGCAGUACAUCAGGCCCA

[0229] UGCAGAAGGAAAUUCUGCUCACAUGUUCUUCUGGGCACUGGGACCAGAAUGUGA

[0230] GCUGCCUUCCCGUGGACUGCGGUGUUCCCGACCCGUCUUUGGUGAACUAUGCAAA

[0231] CUUCUCCUGCUCAGAGGGAACCAAAUUUCUGAAACGCUGCUCAAUCUCUUGUGU

[0232] CCCACCAGCCAAGCUGCAAGGACUGAGCCCAUGGCUGACAUGUCUUGAAGAUGG

[0233] UCUCUGGUCUCUCCCUGAAGUCUACUGCAAGUUGGAGUGUGAUGCUCCCCCUAU

[0234] UAUUCUGAAUGCCAACUUGCUCCUGCCUCACUGCCUCCAGGACAACCACGACGUG

[0235] GGCACCAUCUGCAAAUAUGAAUGCAAACCAGGGUACUAUGUGGCAGAAAGUGCA

[0236] GAGGGUAAAGUCAGGAACAAGCUCCUGAAGAUACAAUGCCUGGAAGGUGGAAUC

[0237] UGGGAGCAAGGCAGCUGCAUUCCUGUGGUGUGUGAGCCACCCCCUCCUGUGUUU

[0238] GAAGGCAUGUAUGAAUGUACCAAUGGCUUCAGCCUGGACAGCCAGUGUGUGCUC

[0239] AACUGUAACCAGGAACGUGAAAAGCUUCCCAUCCUCUGCACUAAAGAGGGCCUG

[0240] UGGACCCAGGAGUUUAAGUUGUGUGAGAAUCUGCAAGGAGAAUGCCCACCACCC

[0241] CCCUCAGAGCUGAAUUCUGUGGAGUACAAAUGUGAACAAGGAUAUGGGAUUGGU

[0242] GCAGUGUGUUCCCCAUUGUGUGUAAUCCCCCCCAGUGACCCCGUGAUGCUACCUG

[0243] AGAAUAUCACUGCUGACACUCUGGAGCACUGGAUGGAACCUGUCAAAGUCCAGA

[0244] GCAUUGUGUGCACUGGCCGGCGUCAAUGGCACCCAGACCCCGUCUUAGUCCACUG

[0245] CAUCCAGUCAUGUGAGCCCUUCCAAGCAGAUGGUUGGUGUGACACUAUCAACAA

[0246] CCGAGCCUACUGCCACUAUGACGGGGGAGACUGCUGCUCUUCCACACUCUCCUCC

[0247] AAGAAGGUCAUUCCAUUUGCUGCUGACUGUGACCUGGAUGAGUGCACCUGCCGG

[0248] GACCCCAAGGCAGAAGAAAAUCAGUAACUGUGGGAACAAGCCCCUCCCUCCACUG

[0249] CCUCAGAGGCAGUAAGAAAGAGAGGCCGACCCAGGAGGAAACAAAGGGUGAAUG

[0250] AAGAAGAACAAUCAUGAAAUGGAAGAAGGAGGAAGAGCAUGAAGGAUCUUAUAA

[0251] GAAAUGCAAGAGGAUAUUGAUAGGUGUGAACUAGUUCAUCAAGUAGCCCAAGUA

[0252] GGAGAGAAUCAUAGGCAAAAGUUUCUUUAAAGUGGCAGUUGAUUAACAUGGAAG

[0253] GGGAAAUAUGAUAGAUAUAUAAGGACCCUCCUCCCUCACUUAUAUUCUAUUAAA

[0254] UCCUAUCCUCAACUCUUGCCCUGCUCUCCGCUCCACCCCCUGCCAACUACUCAGU

[0255] CCCACCCAACUUGUAAACCAAUACCAAAAUACUAGAGGAGAAGUUGGCAGGGAU

[0256] ACUGUUAAUACCCAUUUUGAAUGGAUUGCCAUCUUUCAGAGCUUGUCUGCUCUC

[0257] AACUGGCUCUUUUUCUUUUUGUGUAGUUUCCCUUAAAUAAUGAAGUUAGUUAUU

[0258] AAUUCUUUAUAAGUAUUUAAACAUAAUUAUAUAAAUAUAUUAUAUAUAUUAUA

[0259] UUUUUUGCUGUUUACUAAGCUAAAAAUUAUUCAUUGUUCCACACAUGCUGCUGU GAAGUUCACAUUCAAGAUGAAUGUUGAGACUUUGAGGACAGAAAGGCAACUUAU

[0260] UUUCCCAUCUUUCUAUGGAUGCGGAUUGGCAGGUUGAAUGGGAAGUACAGAAGG

[0261] AGAGAGAGUAAUUAGAUGGAAUUCUGGAUGCUAGCAUGUAAAGCUAAUCAUCUU

[0262] UUUUUUUAUGACCUGGGAGCUGGGCCCAUUUUAUGACCAAGGAGAUGGGGAGUU

[0263] GGAAUGGUGGUACUAAGAGGCAUAGGAAGUUGAGUGUGAAUACCAUUGGUGAUG

[0264] GGUCCAGGAGAACUAGACUAUGGUUCUUGAAUAUCUGUCCACAAAGAAUAUACU

[0265] AACUUUUGUCAACUUCUCAGAACUCCCAACUGGAGUCGGUGAGACCUAGGAUUU

[0266] UCUGCACUUCCACACAUGCCUGUUCCAAGUGUGGCUGUCAGCCAGUCAACAAGUU

[0267] UGUACUAUGGCCCAUUCUCUGAUCACCAGGAUUACAGGAACUCACACACUCCUCA

[0268] UACUUGGCCUGUAGUCCUACUUCUUGUUAGAAGUCUCCAAGUCUGGCCAGUCAC

[0269] AUGACCAAGUGUUGAUUUUUCUGGAGGAAAAAUUUUAUGGAAAUGAUAUAGGG

[0270] GAAAGGUGGGAGGAGAUGAAAGAACAGGCAAGAGCUGUCAGGGUUAAAUCCAGG

[0271] CCCGGGCAUGAGAAUGGAAGUGAUCAGGGAGACUCGGUCCUUGUUCCAAGUCUC

[0272] CAAAGAAGACCAAAGUGGGUCCCUUGAGCAAUGAAGAAUCUGAGAUAAAUUCUC

[0273] UUCAAGUAUCAUGUACAAAAUCUGUGAGCCAGAGAUUUUGACUUGAGCAAGCCA

[0274] UGGAAAUGCAUGGAGCAAGGGUGACACUCUGUGGGGAGACAGAAGAAUUUCAAC

[0275] UAUUUAAUGUCCAUUUUGUUGUUUUUACCCUUUCUUAUCCAAUAGAUGGAAUGC

[0276] ACAUGAAAUGACCAUAUUAAGCCUCUCUCUAUUUACAUCCCAGGCUCACUGGGA

[0277] UGUGAUCUACUGCAGUUACAUUUUCUUGUAACGGUUUCUGGAUUAGACCCUAGG

[0278] GAAAGUGAGUAAGGAGCCAGUUUCUGUUUAACAUUCUAGUUUUACUCAUUUUAG

[0279] GAAGGCUGUGAGUGAGGCUUGUCUCCUUUAAAGUUUCUUCUCCAAUGGAAACCA

[0280] AGAACAGACAAAAUUUAGAGCUCAGCUGUGGUCUCUUCUCAUCUUCUGCUCUUU

[0281] UGCUUUGACCACAGUUUUUCUACUCUUCCCAUCAACACUAGAGCAAUGGCUGUG

[0282] CAAAUAGGAAUAGGAAAUACUACCACAAUGAUAGAAAUAUUAUCCACACUAUCA

[0283] CGUAGGGAAGAACAAUAUCCUGAAAGAGAAUAAAACACGAAUAAGGUGAUGUAC

[0284] CCACAUUAAUCUGUGGGUUUGUGGAAUGAGGGUUGCAAAGUUAUUGGGAAAAGG

[0285] AAAGAGCAGAGUUCACCCAUUCAAAAAAAACCUUUUGUCUACUAAUCUCUAGUG

[0286] UAAAGAAAAUGUAGUUCAGAUACCAUUCAUUGUCUUGGGUCAUGCUUAGUGCCC

[0287] CCAAGAAGACAAACAUAUUUAUUCUUGGGAUUCUGAUAGGCUUCAAUAUGCAAA

[0288] GGACAAUGGAAAAGUUUAGACACUCUAUUUUCAAAAUUUUAUAAACUUGUUUUA

[0289] UUGGGGAAAAUGUCCAAAUUGCUAGACACAUUCUAAGUUCUGCCUUGGAGAAUC

[0290] CUACUUUGUCUGAGAUUGAGGCAGAGGAAUUGUUAUCCUGGGCAUUACUCAGCU

[0291] CAGGAACAUGGAGCCUGUGGUUCAUGCCAGUGUGUGUCUUCAUGCAGUCUCUCC

[0292] ACAAGAGCAACAGUAAGAACAUUUCUGUUUUAAAUUUCAUUUUAAAAUAUUUUA

[0293] UUAUCUGCAAUUCACCACUGCUCUGGGAAAGCAAAAGGAAAGUUCCUGUUGUGU GUGAAGAGCCUCUUAGGCUAUAAGGCUUCCCAGCCAUAGUCAGCUAUAGCUAUU CAGAGACAGCAGGUUCUUCCAGUCUUUGUUCCUGGGACCUGAUGUUUUGAGCAA CUCAGGUCACUGAUAAAGUGGAAGGACUAAGACACUGUGGUCACAGAUCCCAGC AACAUCAACUCACACUCAAUCCAUGUGGUGGUCCACAUUCUGCUACUCUUAUCCA CCCAUGUGGUCAUUGAGAGCCUUUCUCAGAGACUCUUCUGUGUGUUUGAUUGUG CCCAGGUGGCCCAGGGCUAGCUGGCUCUAACAACUAGCAUGACAGCCUCCAAUCA GAAAGGCAGGUAAGGGGACAGGGUGAGGAGAAUGGGCAGAUACUGACAGAAAUU AAAGUAAAGGGAUUGUGAAAGUAAAGAGCUCUUCCUGAUUCUCAUCUUCUCUUU UUUCUAUUACAAGGCAUUGAACUUGGCACUUCCUGUAUUCUUUGUGAUCACUAU UGAGUGCAUUAGUUAACACCCAAGGGGAUGGCUUGAUUGGGAAUGUAGUGAAAG GAGCUGAUCUACUGUAUUGUAAUGUAAAACAGCUACAGCCAGUUAUUUUGUAAG AUUAUAAGUUGUUC AUUAAAAAAUC AGC AC AC AAAAUA ‘ 3

[0294]

[0149] A computation algorithm was used to design a set of top 200 RNA interference siRNA sequences for with target PAPPA2 mRNA, as shown in Table 5.

[0295]

[0150] Table 5. Set of 200 siRNA sequences for RNA interference with target mRNA

[0296] PAPPA2 transcript NM 020318.3

[0297]

[0298]

[0151] Additional target sequences can be generated using computational tools, such as siDIRECT 2.0 (Naito et al., “siDirect 2.0: updated software for designing functional siRNA with reduced seed-dependent off-target effect”, BMC Bioinformatics, 2009, which is incorporated by reference herein in its entirety), siRNA-Finder (Luck et al, “siRNA-Finder (si-Fi) Software for RNAi-Target Design and Off-Target Prediction”, Frontiers in Plant Science, 2019, which is incorporated by reference herein in its entirety), SSD (Jose de Carli et al, “SSD - a free software for designing multimeric mono-, bi- and trivalent shRNAs”, Genet. Mol. Bio., 2020, which is incorporated by reference herein in its entirety) or software such as BLOCK-iT RNAi Designer (Thermo Fisher Scientific). These tools allow for user-defined adjustment of key parameters to optimize siRNA performance.

[0152] To enhance silencing efficiency, the following parameters may be used: (1) an A or U nucleotide at the 5' terminus of the guide strand, and a G or C nucleotide at the 5' terminus of the passenger strand; (2) a general enrichment of A and U nucleotides within the 5' terminal region of both strands; (3) an overall low GC content, such as within the range of 30-50%, while avoiding long stretches of consecutive guanine or cytosine residues; and (4) low internal stability at the 3' terminal region of the guide strand, characterized by the presence of three or more consecutive A or U nucleotides.

[0299]

[0153] Furthermore, sequences may be selected to minimize the formation of stable internal repeats or hairpin structures, as determined by melting temperature (Tm) calculations using the nearest-neighbor thermodynamic model (e.g., as implemented in the Integrated DNA Technologies (IDT) OligoAnalyzer tool).

[0300]

[0154] To mitigate off-target effects, siRNA sequences may be designed with a low melting temperature (Tm) within the 2-8 nucleotide region of the guide strand, such as calculated using the nearest-neighbor thermodynamic model. Additionally, target sequences may be screened against a comprehensive genomic database, such as the NCBI RefSeq database, to identify and exclude sequences with significant homology to non-target transcripts. A minimum of two mismatches, and possibly more, to any non-target transcript may be required to minimize off- target silencing.

[0301]

[0155] Additional considerations for siRNA design include the secondary structure of the target mRNA, the accessibility of the target site, and the ability to achieve isoform-specific targeting. Target site accessibility can be predicted using algorithms such as RNAplfold (Bernhart et al., “Local RNA base pairing probabilities in large sequences”, Bioinformatics, 2006, which is incorporated by reference herein in its entirety). Isoform specific targeting can be achieved by targeting regions of mRNA that are unique to a given isoform.

[0302]

[0156] To test efficacy of siRNA to modulate PAPPA2 mRNA levels, an siRNA sequence duplex may be synthesized with a screening design, such as a 15 / 20 asymmetric duplex with alternating 2’O-methyl / 2’Fluoro base modifications and phosphorothioates on the ends, as shown in FIG. 4. An individual siRNA construct is transfected into human cell line to determine the individual efficacy to modulate cellular PAPPA2 mRNA. High-performing siRNA constructs are selected and tested in animal models.

[0303]

[0157] Example 2: Early prediction of preeclampsia using CD163 driven molecular subtyping of preeclampsia and specific treatment by modulating CD163 level mRNA by siRNA

[0304]

[0158] Using systems and methods of the present disclosure, early molecular markers for specific subtype of preeclampsia (PE) are identified in maternal blood samples from a population at increased risk of preeclampsia (PE) and treatment by modulating level of CD 163 mRNA by siRNA.

[0305]

[0159] As described in Example 1, This is true across all PAPPA2+ preeclampsia and for PAPPA2+ preeclampsia in the group without major risk factors.

[0306]

[0160] For samples not in the top quartile of PAPP A2 expression we see several immune genes in the top 20 most differentially expressed, this applies whether we look at all preeclampsia (FIG 5A) or all hypertensive disorders of pregnancy (FIG 5B). The top differentially expressed gene in both of those groups is CD163, a full list of the top 20 differentially expressed genes in the bottom three quartiles of PAPP A2 expression can be found in Table 6 for all preeclampsia and Table 7 for all hypertensive disorders of pregnancy.

[0307]

[0161] Table 6. Set of 21 differentially expressed genes from the bottom three quartiles of PAPPA2 expression for all preeclampsia.

[0162] Table 7. Set of 21 differentially expressed genes from the bottom three quartiles of PAPPA2 expression for all hypertensive disorders of pregnancy.

[0308]

[0163] To modulate mRNA level of CD163, a transcript sequence NM_004244.6 was used to design a set of candidate siRNA molecules.

[0309]

[0164] NM 004244.6

[0310]

[0165] AGAAUUCUUAGUUGUUUUCUUUAGAAGAACAUUUCUAGGGAAUAAUACA AGAAGAUUUAGGAAUCAUUGAAGUUAUAAAUCUUUGGAAUGAGCAAACUCAGAA

[0311] UGGUGCUACUUGAAGACUCUGGAUCUGCUGACUUCAGAAGACAUUUUGUCAACU UGAGUCCCUUCACCAUUACUGUGGUCUUACUUCUCAGUGCCUGUUUUGUCACCA

[0312] GUUCUCUUGGAGGAACAGACAAGGAGCUGAGGCUAGUGGAUGGUGAAAACAAGU GUAGCGGGAGAGUGGAAGUGAAAGUCCAGGAGGAGUGGGGAACGGUGUGUAAUA

[0313] AUGGCUGGAGCAUGGAAGCGGUCUCUGUGAUUUGUAACCAGCUGGGAUGUCCAA

[0314] CUGCUAUCAAAGCCCCUGGAUGGGCUAAUUCCAGUGCAGGUUCUGGACGCAUUU

[0315] GGAUGGAUCAUGUUUCUUGUCGUGGGAAUGAGUCAGCUCUUUGGGAUUGCAAAC

[0316] AUGAUGGAUGGGGAAAGCAUAGUAACUGUACUCACCAACAAGAUGCUGGAGUGA

[0317] CCUGCUCAGAUGGAUCCAAUUUGGAAAUGAGGCUGACGCGUGGAGGGAAUAUGU

[0318] GUUCUGGAAGAAUAGAGAUCAAAUUCCAAGGACGGUGGGGAACAGUGUGUGAUG

[0319] AUAACUUCAACAUAGAUCAUGCAUCUGUCAUUUGUAGACAACUUGAAUGUGGAA

[0320] GUGCUGUCAGUUUCUCUGGUUCAUCUAAUUUUGGAGAAGGCUCUGGACCAAUCU

[0321] GGUUUGAUGAUCUUAUAUGCAACGGAAAUGAGUCAGCUCUCUGGAACUGCAAAC

[0322] AUCAAGGAUGGGGAAAGCAUAACUGUGAUCAUGCUGAGGAUGCUGGAGUGAUUU

[0323] GCUCAAAGGGAGCAGAUCUGAGCCUGAGACUGGUAGAUGGAGUCACUGAAUGUU

[0324] CAGGAAGAUUAGAAGUGAGAUUCCAAGGAGAAUGGGGGACAAUAUGUGAUGACG

[0325] GCUGGGACAGUUACGAUGCUGCUGUGGCAUGCAAGCAACUGGGAUGUCCAACUG

[0326] CCGUCACAGCCAUUGGUCGAGUUAACGCCAGUAAGGGAUUUGGACACAUCUGGC

[0327] UUGACAGCGUUUCUUGCCAGGGACAUGAACCUGCUAUCUGGCAAUGUAAACACC

[0328] AUGAAUGGGGAAAGCAUUAUUGCAAUCACAAUGAAGAUGCUGGCGUGACAUGUU

[0329] CUGAUGGAUCAGAUCUGGAGCUAAGACUUAGAGGUGGAGGCAGCCGCUGUGCUG

[0330] GGACAGUUGAGGUGGAGAUUCAGAGACUGUUAGGGAAGGUGUGUGACAGAGGCU

[0331] GGGGACUGAAAGAAGCUGAUGUGGUUUGCAGGCAGCUGGGAUGUGGAUCUGCAC

[0332] UCAAAACAUCUUAUCAAGUGUACUCCAAAAUCCAGGCAACAAACACAUGGCUGU

[0333] UUCUAAGUAGCUGUAACGGAAAUGAAACUUCUCUUUGGGACUGCAAGAACUGGC

[0334] AAUGGGGUGGACUUACCUGUGAUCACUAUGAAGAAGCCAAAAUUACCUGCUCAG

[0335] CCCACAGGGAACCCAGACUGGUUGGAGGGGACAUUCCCUGUUCUGGACGUGUUG

[0336] AAGUGAAGCAUGGUGACACGUGGGGCUCCAUCUGUGAUUCGGACUUCUCUCUGG

[0337] AAGCUGCCAGCGUUCUAUGCAGGGAAUUACAGUGUGGCACAGUUGUCUCUAUCC

[0338] UGGGGGGAGCUCACUUUGGAGAGGGAAAUGGACAGAUCUGGGCUGAAGAAUUCC

[0339] AGUGUGAGGGACAUGAGUCCCAUCUUUCACUCUGCCCAGUAGCACCCCGCCCAGA

[0340] AGGAACUUGUAGCCACAGCAGGGAUGUUGGAGUAGUCUGCUCAAGAUACACAGA

[0341] AAUUCGCUUGGUGAAUGGCAAGACCCCGUGUGAGGGCAGAGUGGAGCUCAAAAC

[0342] GCUUGGUGCCUGGGGAUCCCUCUGUAACUCUCACUGGGACAUAGAAGAUGCCCA

[0343] UGUUCUUUGCCAGCAGCUUAAAUGUGGAGUUGCCCUUUCUACCCCAGGAGGAGC

[0344] ACGUUUUGGAAAAGGAAAUGGUCAGAUCUGGAGGCAUAUGUUUCACUGCACUGG

[0345] GACUGAGCAGCACAUGGGAGAUUGUCCUGUAACUGCUCUAGGUGCUUCAUUAUG

[0346] UCCUUCAGAGCAAGUGGCCUCUGUAAUCUGCUCAGGAAACCAGUCCCAAACACUG UCCUCGUGCAAUUCAUCGUCUUUGGGCCCAACAAGGCCUACCAUUCCAGAAGAAA

[0347] GUGCUGUGGCCUGCAUAGAGAGUGGUCAACUUCGCCUGGUAAAUGGAGGAGGUC

[0348] GCUGUGCUGGGAGAGUAGAGAUCUAUCAUGAGGGCUCCUGGGGCACCAUCUGUG

[0349] AUGACAGCUGGGACCUGAGUGAUGCCCACGUGGUUUGCAGACAGCUGGGCUGUG

[0350] GAGAGGCCAUUAAUGCCACUGGUUCUGCUCAUUUUGGGGAAGGAACAGGGCCCA

[0351] UCUGGCUGGAUGAGAUGAAAUGCAAUGGAAAAGAAUCCCGCAUUUGGCAGUGCC

[0352] AUUCACACGGCUGGGGGCAGCAAAAUUGCAGGCACAAGGAGGAUGCGGGAGUUA

[0353] UCUGCUCAGAAUUCAUGUCUCUGAGACUGACCAGUGAAGCCAGCAGAGAGGCCU

[0354] GUGCAGGGCGUCUGGAAGUUUUUUACAAUGGAGCUUGGGGCACUGUUGGCAAGA

[0355] GUAGCAUGUCUGAAACCACUGUGGGUGUGGUGUGCAGGCAGCUGGGCUGUGCAG

[0356] ACAAAGGGAAAAUCAACCCUGCAUCUUUAGACAAGGCCAUGUCCAUUCCCAUGU

[0357] GGGUGGACAAUGUUCAGUGUCCAAAAGGACCUGACACGCUGUGGCAGUGCCCAU

[0358] CAUCUCCAUGGGAGAAGAGACUGGCCAGCCCCUCGGAGGAGACCUGGAUCACAU

[0359] GUGACAACAAGAUAAGACUUCAGGAAGGACCCACUUCCUGUUCUGGACGUGUGG

[0360] AGAUCUGGCAUGGAGGUUCCUGGGGGACAGUGUGUGAUGACUCUUGGGACUUGG

[0361] ACGAUGCUCAGGUGGUGUGUCAACAACUUGGCUGUGGUCCAGCUUUGAAAGCAU

[0362] UCAAAGAAGCAGAGUUUGGUCAGGGGACUGGACCGAUAUGGCUCAAUGAAGUGA

[0363] AGUGCAAAGGGAAUGAGUCUUCCUUGUGGGAUUGUCCUGCCAGACGCUGGGGCC

[0364] AUAGUGAGUGUGGGCACAAGGAAGACGCUGCAGUGAAUUGCACAGAUAUUUCAG

[0365] UGCAGAAAACCCCACAAAAAGCCACAACAGGUCGCUCAUCCCGUCAGUCAUCCUU

[0366] UAUUGCAGUCGGGAUCCUUGGGGUUGUUCUGUUGGCCAUUUUCGUCGCAUUAUU

[0367] CUUCUUGACUAAAAAGCGAAGACAGAGACAGCGGCUUGCAGUUUCCUCAAGAGG

[0368] AGAGAACUUAGUCCACCAAAUUCAAUACCGGGAGAUGAAUUCUUGCCUGAAUGC

[0369] AGAUGAUCUGGACCUAAUGAAUUCCUCAGAAAAUUCCCAUGAGUCAGCUGAUUU

[0370] CAGUGCUGCUGAACUAAUUUCUGUGUCUAAAUUUCUUCCUAUUUCUGGAAUGGA

[0371] AAAGGAGGCCAUUCUGAGCCACACUGAAAAGGAAAAUGGGAAUUUAUAACCCAG

[0372] UGAGUUCAGCCUUUAAGAUACCUUGAUGAAGACCUGGACUAUUGAAUGGAGCAG

[0373] AAAUUCACCUCUCUCACUGACUAUUACAGUUGCAUUUUUAUGGAGUUCUUCUUC

[0374] UCCUAGGAUUCCUAAGACUGCUGCUGAAUUUAUAAAAAUUAAGUUUGUGAAUGU

[0375] GACUACUUAGUGGUGUAUAUGAGACUUUCAAGGGAAUUAAAUAAAUAAAUAAGA

[0376] AUGUUAUUGAUUUGAGUUUGCUUUAAUUACUUGUCCUUAAUUCUAUUAAUUUCU

[0377] AAAUGGGCUUCCUAAUUUUUUGUAGAGUUUCCUAGAUGUAUUAUAAUGUGUUUU

[0378] AUUUGACAGUGUUUCAAUUUGCAUAUACAGUACUGUAUAUUUUUUCUUAUUUGG

[0379] UUUGAAUAAUUUUCCUAUUACCAAAUAAAAAUAAAUUUAUUUUUACUUUAGUUU

[0380] UUCUAAGACAGGAAAAGUUAAUGAUAUUGAAGGGUCUGUAAAUAAUAUAUGGCU AACUUUAUAAGGCAUGACUCACAACGAUUCUUUAACUGCUUUUUGUUACUGUAA UUCUGUUCACUAGAAUAAAAUGCAGAGCCACACCUGGUGAGGGCACAAAGA.

[0381]

[0166] A computation algorithm was used to design a set of top 200 RNA interference siRNA sequences for with target Cd 163 mRNA, as shown in Table 8.

[0382]

[0167] Table 8: Set of 200 siRNA sequences for RNA interference with target mRNA CD163 transcript.

[0383]

[0384]

[0168] To test efficacy of siRNA for modulating CD 163 mRNA levels, an siRNA sequence duplex is synthesized with a screening design, such as a 15 / 20 asymmetric duplex with alternating 2’0-methyl / 2’Fluoro base modifications and phosphorothioates on the ends, as shown in FIG. 4. An individual siRNA construct is transfected into a human cell line to determine the individual efficacy to modulate cellular CD 163 mRNA. High-performing siRNA constructs are selected and tested in animal models.

[0385]

[0169] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A method comprising modulating an mRNA expression level of a pregnant subject, comprising modulating a transcription factor of said pregnant subject, thereby treating said pregnant subject for a pregnancy-related state.

2. The method of claim 1, wherein said mRNA expression level corresponds to PAPPA2.

3. The method of claim 2, further comprising modulating said transcription factor of PAPPA2, wherein said transcription factor of PAPP A2 is selected from the group listed in Table3.

4. The method of claim 2, further comprising modulating said transcription factor of PAPPA2 at least in part by administering an siRNA to said pregnant subject.

5. The method of claim 4, wherein said siRNA comprises a sequence selected from the group listed in Table 5.

6. The method of claim 2, further comprising modulating said transcription factor of PAPPA2 at least in part by administering an anti-sense oligonucleotide (ASO) to said pregnant subject.

7. The method of claim 1, wherein said mRNA expression level corresponds to CD 163.

8. The method of claim 7, further comprising modulating said transcription factor of CD163, wherein said transcription factor of CD163 is selected from the group listed in Table 3.

9. The method of claim 7, further comprising modulating said transcription factor of CD 163 at least in part by administering an siRNA to said pregnant subject.

10. The method of claim 9, wherein said siRNA comprises a sequence selected from the group listed in Table 8.

11. The method of claim 7, further comprising modulating said transcription factor of CD 163 at least in part by administering an ASO to said pregnant subject.

12. The method of claim 1, wherein said pregnancy-related state comprises pre-eclampsia.

13. A composition for modulating an mRNA expression level of a pregnant subject thereby treating said pregnant subject for a pregnancy -related state, comprising an siRNA.

14. The composition of claim 13, wherein said siRNA comprises a sequence selected from the group listed in Table 5 and Table 8.

15. The composition of claim 14, wherein said siRNA comprises a sequence selected from the group listed in Table 5.

16. The composition of claim 14, wherein said siRNA comprises a sequence selected from the group listed in Table 8.

17. A composition for modulating an mRNA expression level of a pregnant subject thereby treating said pregnant subject for a pregnancy -related state, comprising an ASO.

18. A method for detect a presence or elevated risk of a molecular subtype of a pregnancy- related state of a pregnant subject, comprising assaying a cell-free biological sample derived from said pregnant subject to detect a set of biomarkers, and analyzing said set of biomarkers to detect said presence or elevated risk of said molecular subtype of said pregnancy-related state, wherein said pregnant subject is asymptomatic for said pregnancy -related state.

19. The method of claim 18, wherein said pregnancy-related state is a subtype of preeclampsia and wherein said plurality of distinct pregnancy-related states include at least two distinct subtypes of preeclampsia.

20. The method of claim 18, wherein said subtype of preeclampsia is a molecular subtype of preeclampsia, and wherein said at least two distinct subtypes of preeclampsia include at least two distinct molecular subtypes of preeclampsia.

21. The method of claim 18, wherein said molecular subtype of preeclampsia is selected from the group consisting of history of chronic or pre-existing hypertension, superimposed preeclampsia, presence or history of gestational hypertension, presence or history of preeclampsia without severe features, presence or history of preeclampsia with severe features, presence or history of eclampsia, and presence or history of HELLP syndrome.

22. The method of claim 18, wherein said molecular subtype of preeclampsia is pre-term preeclampsia or term preeclampsia with severe features, and wherein said set of biomarkers comprises a genomic locus associated with pre-term preeclampsia or term preeclampsia with severe features.

23. The method of claim 18, wherein said genomic locus associated with pre-term preeclampsia or term preeclampsia with severe features is selected from the group consisting of genes listed in Table 1, and genes listed in Table 4.

24. The method of claim 18, wherein said genomic locus associated with term preeclampsia without severe features or gestation hypertension selected from the group consisting of genes listed in Table 2, genes listed in Table 6, and genes listed in Table 7.

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