Methods for identifying and monitoring pregnant women at risk of preeclampsia

Abstract

Methods and kits for identifying an increased risk of developing preeclampsia in a pregnant woman based on expression pattern of non-coding RNAs in body fluids are provided. In particular, the methods provide information for identifying a pregnant woman as being at risk of developing preeclampsia by analyzing the pattern of non-coding RNAs in body fluids during early stages of pregnancy.

Claims

1. A method for determining a non-coding RNA (ncRNA) expression pattern in plasma or serum, the method comprising: (i) obtaining a plasma or serum sample from a pregnant woman in the first trimester of the pregnancy or in weeks 10-14 of the pregnancy; and (ii) measuring the levels of a at least ten non-coding RNAs (ncRNAs) selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in the plasma or serum sample from the pregnant woman.

2. The method of claim 1, wherein said measuring levels of at least ten ncRNAs comprises extracting RNA from the plasma or serum sample, reverse transcribing said RNA into cDNA, and measuring the amount of said cDNA using quantitative-PCR.

3. The method of claim 1, wherein said measuring levels of at least ten ncRNAs comprises applying at least ten detectably labeled oligonucleotides, each capable of specifically hybridizing to one of said ncRNAs.

4. The method of claim 3, wherein said detectably labeled oligonucleotides are immobilized to a solid surface.

5. The method of claim 1, wherein said at least ten ncRNAs comprises all 25 of the ncRNAs corresponding to SEQ ID NOs: 1-25.

6. A system for determining whether a pregnant woman is at risk of developing preeclampsia, the system comprising: detectably-labeled probes for measuring the levels of a plurality of ncRNAs selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in a plasma or serum sample obtained from the pregnant woman, wherein the pregnant woman is in the first trimester of the pregnancy or in week 10 to 14 of the pregnancy; and a computer software embodied on a computer readable storage medium, the computer software employing a pattern analyzing algorithm for carrying out a comparison between the ncRNA expression pattern of the pregnant woman and reference values, wherein the computer software: (i) compares the ncRNA expression pattern of the pregnant woman to non-preeclampsia reference pattern, preeclampsia reference pattern or both; and (ii) based on the comparison, outputs an indication whether the pregnant woman is at risk of developing preeclampsia, by detecting at least one of: increased expression levels of one or more ncRNA corresponding to SEQ ID NOs: 1-2, 5, 7-10, 13, 16-17, 19-23 and 25 compared to non-preeclampsia reference values; and decreased expression levels of one or more ncRNA corresponding to SEQ ID NOs: 3-4, 6, 11-12, 14-15, 18 and 24 compared to non-preeclampsia reference values.

7. The system of claim 6, wherein the computer software outputs a score indicative of the risk of the pregnant woman to develop preeclampsia, wherein a score above a predefined threshold is indicative of risk of developing preeclampsia.

8. The system of claim 6, further comprising oligonucleotide primer pairs for reverse transcribing RNA of said plurality of ncRNAs from the plasma or serum sample into cDNA, and performing quantitative-PCR of the cDNA.

9. The system of claim 6, wherein said detectably-labeled probes comprise oligonucleotides for detecting amplification products in quantitative-PCR.

10. The system of claim 6, wherein said detectably-labeled probes for measuring levels of a plurality of ncRNAs comprise a plurality of detectably-labeled oligonucleotides that specifically hybridize to said ncRNAs.

11. The system of claim 10, wherein said detectably-labeled oligonucleotides are immobilized to a surface.

12. The system of claim 6, further comprising means for extracting RNA from the plasma or serum sample.

13. The system of claim 6, wherein said plurality of ncRNAs comprises at least five ncRNAs.

14. The system of claim 6, wherein said plurality of ncRNAs comprises at least ten ncRNAs.

15. A method for identifying and managing a pregnant woman at risk of developing preeclampsia, the method comprising: (i) measuring the levels of a plurality of non-coding RNAs (ncRNAs) selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in a plasma or serum sample from the pregnant woman, wherein the pregnant woman is in the first trimester of the pregnancy or in week 10 to 14 of the pregnancy, to thereby obtain a ncRNA expression pattern of said pregnant woman; (ii) comparing the ncRNA expression pattern of the pregnant woman to a non-preeclampsia reference pattern; (iii) characterizing the pregnant woman as being at risk of developing preeclampsia when the ncRNA expression pattern of the pregnant woman is determined to be different from the non-preeclampsia reference pattern by detecting at least one of: increased expression levels of one or more ncRNA corresponding to SEQ ID NOs: 1-2, 5, 7-10, 13, 16-17, 19-23 and 25 compared to non-preeclampsia reference values, and decreased expression levels of one or more ncRNA corresponding to SEQ ID NOs: 3-4, 6, 11-12, 14-15, 18 and 24 compared to the non-preeclampsia reference values; iv) optionally conducting preeclampsia follow-up testing comprising one or more of blood tests, urine tests and blood pressure measurements; and v) administering to the pregnant woman characterized as being at risk of developing preeclampsia a therapy to manage preeclampsia comprising at least one of bed rest and diet changes.

16. The method of claim 15, wherein said plurality of ncRNAs comprises at least five ncRNAs.

17. The method of claim 15, wherein said plurality of ncRNAs comprises at least ten ncRNAs.

18. The method of claim 15, wherein said plurality of ncRNAs comprises all 25 of the ncRNAs corresponding to SEQ ID NOs: 1-25.

Description

BRIEF DESCRIPTION OF THE FIGURES

(1) FIG. 1. Principal component analysis (PCA) plot. The samples are shown in the 2D plane spanned by their first two principal components. (A) Samples before batch effects removal and (B) after batch effects removal. Outlier samples are highlighted by a circle. “control”—samples from healthy women; “PE”—samples from women that developed preeclampsia.

(2) FIG. 2. Plots of normalized counts for the 25 differentially expressed transcripts in the top 100 most abundant transcripts in preeclampsia (marked as PE) and control samples. The Y axis represents normalized counts.

(3) FIG. 3. Correlation between microRNA 4433b (SEQ ID NO: 8) and maternal clinical characteristics. Plots of mir-4433b normalized counts obtained by RNA-Seq of circulating non-coding RNA extracted from first trimester pregnant women plasma, versus clinical features of these women: (A) uterine artery pulsatility index (UT PI, r=0.395, p-value=0.016); (B) mean arterial pressure (MAP, r=0.442, p-value=0.003).

(4) FIG. 4. Statistic histograms obtained by 100 repeats of the classification procedure described in Example 2 (i.e., on 100 random test sets). Means and medians are indicated.

(5) FIG. 5. Normalized counts for the 25 differentially expressed transcripts in preeclampsia (PE) vs. control samples in the first and second trimesters. Regression lines for both groups are indicated, to display expression trend between the two trimesters. The X axis represents the different trimesters (1 and 2). The Y axis represents normalized counts.

(6) FIG. 6. Relative expression of miR-22, miR-10b, miR-146b, and miR-151a in human first trimester cytotrophoblast cell line (HTR8/SVneo cells) cultured under both hypoxic (PE model) and normal (control) conditions. Expressions were measured by RT-qPCR. Control expressions were set to 1.

DETAILED DESCRIPTION OF THE INVENTION

(7) The present invention provides, according to some aspects, methods and kits for identification and monitoring of pregnant women having an increased risk of developing preeclampsia.

(8) The present invention proposes a unique marker of preeclampsia—an expression pattern of a set of ncRNAs that were found to be differentially expressed in first trimester body fluid samples of pregnant women that developed preeclampsia compared to similar samples of women that did not develop preeclampsia.

(9) In certain embodiments of the present invention, preeclampsia is defined as hypertension (systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg on at least two occasions, 6 hours apart), and proteinuria (>300 milligrams in a 24 hour urine collection or one dipstick measurement >2+).

(10) In some embodiments, the woman to be tested by the methods and kits of the present invention is asymptomatic and the method/kit is applied for evaluating the risk of said woman to develop preeclampsia.

(11) In other embodiments, the woman shows one or more signs characteristic of preeclampsia (e.g., increased blood pressure) and the method/kit is applied for confirming the diagnosis, or for aiding the diagnosis of preeclampsia in said woman.

(12) The woman to be tested according to the present invention is at early stages of the pregnancy, up to week 15. In some embodiments, the woman to be tested is in the first trimester of the pregnancy, which is typically defined as week 1 through week 12. In some embodiments, the woman to be tested is in weeks 12-14. In additional embodiments, for example when the method is applied for confirming or aiding the diagnosis of a woman showing some symptoms of preeclampsia, the woman may be in a more advanced stage of the pregnancy.

(13) As used herein, the term “increased”, when referring to a risk of developing preeclampsia, refers to increased risk compared to the normal risk in the population.

(14) According to some embodiments, the method of the present invention comprises the following steps: (i) measuring the levels of a plurality of ncRNAs (small ncRNAs according to some embodiments) selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in a body fluid sample from said pregnant woman, thereby obtaining a ncRNA pattern of the pregnant woman; (ii) comparing the ncRNA pattern of the pregnant woman to a non-preeclampsia reference pattern; and (iii) characterizing the pregnant woman as being at an increased risk of developing preeclampsia wherein the ncRNA pattern of the pregnant woman is determined to be different from the non-preeclampsia reference pattern by detecting at least one of: increased levels of the one or more ncRNA set forth in SEQ ID NOs: 1-2, 5, 7-10, 13, 16-17, 19-23 and 25 compared to non-preeclampsia reference values; and decreased values of the one or more ncRNA corresponding to SEQ ID NOs: 3-4, 6, 11-12, 14-15, 18 and 24 compared to non-preeclampsia reference values.

(15) In some embodiments, the method further comprises informing the pregnant woman characterized as being at risk of developing preeclampsia, or a health care professional involved in the prenatal care of said pregnant woman, that said pregnant woman must be monitored for development of symptoms of preeclampsia. A health care professional may include, for example, a gynecologist, a family doctor or a nurse.

(16) In some embodiments, a method is provided for determining a ncRNA expression pattern in the plasma of a pregnant woman in the first trimester of the pregnancy, the method comprising: (i) obtaining a plasma sample from the pregnant woman in the first trimester of the pregnancy; (ii) measuring levels of a plurality of ncRNAs selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in the plasma sample from said pregnant woman; (iii) determining whether the level of the one or more ncRNA corresponding to SEQ ID NOs: 1-2, 5, 7-10, 13, 16-17, 19-23 and 25 is increased compared to corresponding non-preeclampsia reference values, and whether the level of the one or more ncRNA corresponding to SEQ ID NOs: 3-4, 6, 11-12, 14-15, 18 and 24 is decreased compared to corresponding non-preeclampsia reference values, thereby determining the ncRNA expression pattern in the plasma of the pregnant woman in the first trimester of the pregnancy.

(17) In additional embodiments, a method is provided for identifying and treating a pregnant woman at risk of developing preeclampsia, the method comprising: (i) measuring levels of a plurality of ncRNAs selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in a body fluid sample from the pregnant woman to obtain a ncRNA expression pattern of said pregnant woman; (ii) comparing the ncRNA expression pattern of the pregnant woman to a non-preeclampsia reference pattern; (iii) characterizing the pregnant woman as being at risk of developing preeclampsia wherein the ncRNA expression pattern of the pregnant woman is determined to be different from the non-preeclampsia reference pattern by detecting at least one of: increased expression levels of the one or more ncRNA corresponding to SEQ ID NOs: 1-2, 5, 7-10, 13, 16-17, 19-23 and 25 compared to non-preeclampsia reference values; and decreased expression levels of the one or more ncRNA corresponding to SEQ ID NOs: 3-4, 6, 11-12, 14-15, 18 and 24 compared to the non-preeclampsia reference values; and iv) treating the pregnant woman to decrease the pregnant woman's risk of developing preeclampsia.

(18) Treatment to reduce the risk of developing preeclampsia, or to reduce the severity of preeclampsia, may include rest, close monitoring and diet changes. Close monitoring may include blood tests, urine tests and monitoring of blood pressure more frequently than what is typically recommended for pregnancy.

(19) In some embodiments, a kit is provided for determining whether a pregnant woman is at risk of developing preeclampsia, the kit comprising: (a) probes for measuring levels of a plurality of ncRNAs selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in a body fluid sample obtained from a pregnant woman in need for assessment of risk of preeclampsia; (b) written material specifying non-preeclampsia reference values for each of said plurality of ncRNAs corresponding to SEQ ID NOs: 1-25; and (c) instruction manual for characterizing the pregnant woman as being at risk of developing preeclampsia wherein the ncRNAs expression pattern of the pregnant woman is determined to be different from the non-preeclampsia reference values by detecting at least one of: increased expression levels of the one or more ncRNA corresponding to SEQ ID NOs: 1-2, 5, 7-10, 13, 16-17, 19-23 and 25 compared to corresponding non-preeclampsia reference values; and decreased expression levels of the one or more ncRNA corresponding to SEQ ID NOs: 3-4, 6, 11-12, 14-15, 18 and 24 compared to corresponding non-preeclampsia reference values.

(20) In some embodiments, the kit further including, in at least one of said written material and instruction manual, a recommendation for a pregnant woman found to be at risk of developing preeclampsia to be monitored for symptoms of preeclampsia.

(21) As used herein, a “plurality” indicates at least two. In some embodiments, a plurality refers to at least three ncRNAs, for example at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four or twenty-five ncRNAs. Each possibility represents a separate embodiment of the present invention.

(22) As referred to herein, the terms “polynucleotide molecules”, “oligonucleotide”, “polynucleotide”, “nucleic acid” and “nucleotide” sequences may interchangeably be used. The terms are directed to polymers of deoxyribonucleotides (DNA), ribonucleotides (RNA), and modified forms thereof in the form of a separate fragment or as a component of a larger construct, linear or branched, single stranded (ss), double stranded (ds), triple stranded (ts), or hybrids thereof. The term also encompasses RNA/DNA hybrids. The polynucleotides may be, for example, sense and antisense oligonucleotide or polynucleotide sequences of DNA or RNA. The DNA or RNA molecules may be, for example, but are not limited to: complementary DNA (cDNA), genomic DNA, synthesized DNA, recombinant DNA, or a hybrid thereof or an RNA molecule such as miRNA. Accordingly, as used herein, the terms “polynucleotide molecules”, “oligonucleotide”, “polynucleotide”, “nucleic acid” and “nucleotide” sequences are meant to refer to both DNA and RNA molecules. The terms further include oligonucleotides composed of naturally occurring bases, sugars, and covalent inter nucleoside linkages, as well as oligonucleotides having non-naturally occurring portions, which function similarly to respective naturally occurring portions.

(23) The terms “non-coding RNA” (typically abbreviated “ncRNA”) refers to untranslated RNA molecules that are part of the transcribed genomic output. Some of these molecules were reported to have functional roles in cells, for example, in regulation of gene expression. Non-coding RNAs include long non-coding RNA molecules (typically more than 200 nucleotides in length), such as long intergenic non-coding RNAs (lincRNAs), and small non-coding RNA molecules. Small ncRNAs include microRNAs (miRNA), which are about 22 nucleotides long RNA molecules that in animals regulate gene expression post-transcriptionally in a sequence-specific manner by facilitating messenger RNA (mRNA) degradation or by controlling translation. Other small ncRNAs include: PIWI-interacting RNA (piRNA), which are about 28 nucleotides long RNA molecules involved in transposon repression and DNA methylation; small nucleolar RNA (snoRNA), which are about 60-300 nucleotides long, components of small nucleolar ribonucleoproteins that modulate biogenesis and activity of ribosomes by post-transcriptional modifications of ribosomal RNA (rRNA); small nuclear RNA (snRNA), which are about 150 nucleotides long RNA molecules that facilitate mRNA splicing and regulate transcription factors; and transfer RNA (tRNA), typically 73-94 nucleotides long, which are the most abundant small ncRNA and play a role in translation.

(24) The term “complementary DNA”, abbreviated “cDNA”, refers to a DNA sequence complementary to a mature RNA molecule, and is therefore a copy of the intron-free biologically active RNA. The term also encompasses a DNA molecule produced by reverse transcription of an RNA molecule.

(25) The present invention is based on determining the level of at least two non-coding RNA molecules selected from ncRNAs corresponding to SEQ ID NOs: 1-25. SEQ ID NOs: 1-25 are cDNA sequences of the transcripts listed in Table 1 (see the Examples section below). It is understood that in a body fluid sample that is analyzed according to the methods of the present invention the polynucleotide molecules are RNA molecules, in which the sequences are identical to the cDNA sequences except for T nucleotides being replaced with U nucleotides. The term “ncRNA corresponding to” therefore encompasses both the RNA molecules themselves, which can be detected by known methods such as array-based methods, and cDNA generated based on the ncRNA molecules having sequences as set forth in SEQ ID NOs: 1-25. The latter can be detected, for example, using array-based methods as well as quantitative PCR methods.

(26) The ncRNAs analyzed according to the methods of the present invention originate from a body fluid sample. According to some embodiments, the body fluid sample is selected from the group consisting of: whole blood, plasma and serum. Each possibility represents a separate embodiment of the present invention. Thus, according to some embodiments, the ncRNA molecules analyzed according to the methods of the present invention are “circulating” ncRNA molecules, namely, present in circulating blood (in the plasma/serum fractions)

(27) The body fluid sample may be obtained or collected from a subject by conventional methods. The body fluid sample may be treated prior to being subjected to the methods of the present invention. For example, according to some embodiments, the sample is treated to remove cells and cell debris. For removing cells or debris of cells from a bodily fluid sample, the cells or debris of cells may be precipitated by centrifugation and the supernatant is taken for determining the levels of the ncRNAs as disclosed herein. Alternatively, cells can be removed by filtration.

(28) According to embodiments of the present invention, the body fluid samples are collected from women at the first trimester of the pregnancy, typically between weeks 10-15, for example between weeks 10-14, or 10-13.

(29) Measuring Levels of ncRNAs:

(30) As used herein, determination of a “level” of a particular small ncRNA or long ncRNA may refer to determining concentration, expressed, for example, as nanograms/milliliter (ng/ml). In some embodiments, determination of a “level” refers to determining intensity of a signal collected from a probe that represents the amount or concentration of the ncRNA in the sample. In additional embodiments, determination of a “level” refers to calculating an index that represents the amount or concentration of the ncRNA in the sample (for example, determining a Ct value of a real-time PCR reaction). Measuring the levels of the RNA molecules described herein can be performed by any method known in the art. Non-limiting examples include reverse-transcription and quantitative amplification, microarray, NGS (deep sequencing) and Northern blot. Each possibility represents a separate embodiment of the present invention. In some embodiments, measuring a level of a certain ncRNA refers to measuring a level of a fragment thereof, for example a fragment of between 100-200 bps, such as between 140-160 bps. Primers, probes or other molecules may be designed for the detection and measurement of a single fragment or a plurality of fragments of a certain ncRNA, or tiled across the ncRNA such that all of the ncRNA can be detected and measured.

(31) As used herein, “comparing”, when referring to expression levels of a tested sample versus reference pattern/values includes comparison of values such as concentrations, or Ct values or other indices indicating expression levels. For example, in some embodiments, a pattern of Ct values is generated for a tested sample and compared to a reference pattern of Ct values. As another example, a reference pattern of fluorescence units is generated for a tested sample and compared to a reference pattern of fluorescence units.

(32) Typically, the method initially includes extracting total RNA from the body fluid sample.

(33) In some embodiments, the method includes reverse-transcribing the RNA into cDNA, either specifically (e.g., using primers specific to each ncRNA to be measured) or non-specifically (e.g., using poly-A tailing and universal oligo-d(T) primer). Each possibility represents a separate embodiment of the present invention.

(34) In some embodiments, the method further includes quantitatively-amplifying from the cDNA each of the plurality of ncRNA (i.e., DNA sequences corresponding to those of the RNA).

(35) In some embodiment, the quantitative amplification is carried out using labeled primers, e.g., fluorescently labeled primer, to enable detection and quantification of the amplification product, and accordingly the level of the ncRNA in the body fluid sample.

(36) In additional embodiments, detection and quantification of the amplification product is carried out by adding fluorescently labeled oligonucleotide probes to the amplification reaction.

(37) In some embodiment, the method includes fluorescently-labeling the ncRNAs following extraction from the body fluid sample, or the corresponding cDNA, and hybridizing the fluorescently-labeled molecules to an array comprising a plurality of oligonucleotide probes. In some embodiments, the method further includes scanning the array to detect binding of the oligonucleotide probes, and accordingly determining the level of the ncRNA in the original sample. In some embodiments, the method includes comparing the test sample hybridization profile to a reference hybridization profile representing preeclampsia and/or to a reference hybridization profile representing non-preeclampsia. In some embodiments, the method further comprises reverse-transcribing and amplifying the target ncRNAs prior to hybridization with the microarray.

(38) In some embodiments, the method includes separating the ncRNAs following extraction from the body fluid sample, or their corresponding DNA amplification products by gel electrophoresis, transferring to a membrane, probing with oligonucleotide probes capable of specific hybridization to each ncRNA and detecting and quantifying the hybridization to determine the level of the ncRNAs in the original body fluid sample.

(39) As used herein, a “primer” defines an oligonucleotide which is capable of annealing to (hybridizing with) a target sequence, thereby creating a double stranded region which can serve as an initiation point for DNA synthesis under suitable conditions. The terminology “primer pair” refers to a pair of oligonucleotides which are used together in amplifying a selected nucleic acid sequence by PCR. As commonly known in the art, the primers may be designed to bind to a complementary sequence under selected conditions.

(40) In some embodiments, the methods disclosed herein involve simultaneous amplification of more than one target sequence (e.g., more than one ncRNA) in the same reaction mixture, a process known as multiplex amplification or co-amplification. This process requires simultaneous use of a plurality of primer pairs. As known in the art, the primers may be designed such that they can work at the same annealing temperature during amplification. In some embodiments, primers with similar melting temperature (Tm) are used in the method disclosed herein. A Tm variation of between about 3°-5° C. is considered acceptable for primers used in a pool.

(41) In other embodiments, each ncRNA is amplified in a separate reaction mixture.

(42) In some embodiments, the methods of the present invention employ quantitative amplification to measure the levels of ncRNAs. Quantitative amplification typically involves the use of fluorescent probes for detection of the amplification product. The fluorescent signal emitted from the probe correlates with the amount of the amplification product, i.e., the signal increases as the amplification product accumulates.

(43) “Probes” according to the present invention encompass probes for detection of amplification products in quantitative amplification such as real-time PCR, as well as probes for detection of polynucleotide sequences of interest in an array-based assay. In the context of quantitative amplification, “probes” encompass fluorophores such as SYBR® Green, which generally bind the amplification product in a sequence-independent manner, as well as oligonucleotide probes. In the context of array-based quantification of ncRNAs of interest, “probes” typically refers to oligonucleotide probes. As used herein, the term “oligonucleotide probes” refers to oligonucleotides which are complementary to specific sub-sequences within a nucleic acid sequence of interest, for example, within the sequence of a ncRNA or a corresponding cDNA, and which facilitate the detection and quantification of the nucleic acid of interest. In some embodiments, the oligonucleotide probes are detectably-labeled. The oligonucleotide probes typically selectively hybridize to their target sequences.

(44) As used herein, “selectively hybridize to” (as well as “selective hybridization,” “specifically hybridize to,” and “specific hybridization”) refers to the binding, duplexing, or hybridizing of a nucleic acid molecule (such as a primer or a probe) preferentially to a particular complementary nucleotide sequence under stringent conditions. The term “stringent conditions” refers to conditions under which a nucleic acid molecule will hybridize preferentially to its target sequence and to a lesser extent to, or not at all to, other non-target sequences. A “stringent hybridization” in the context of nucleic acid hybridization is sequence-dependent, and differs under different conditions, as known in the art.

(45) Polynucleotide probes may vary in length. In some embodiments, the polynucleotide probes may include between 15-30 bases. In additional embodiments, the polynucleotide probes may include between 25-30 bases. In some embodiments, the polynucleotide probes may include between 20-30 bases.

(46) For quantitative PCR amplification, polynucleotide probes may be designed to bind to either strand of the template. Additional considerations include the Tm of the polynucleotide probes, which should preferably be compatible to that of the primers. Computer software may be used for designing the primers and probes.

(47) The methods of the present invention comprise according to some embodiments measuring levels of at least two non-coding RNAs selected from ncRNAs corresponding to SEQ ID NOs: 1-25 in a body fluid sample from a pregnant woman in the first trimester of the pregnancy.

(48) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 1 and at least one more ncRNA corresponding to SEQ ID NOs: 2-25.

(49) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 2 and at least one more ncRNA corresponding to SEQ ID NOs: 1, 3-25.

(50) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 3 and at least one more ncRNA corresponding to SEQ ID NOs: 1-2, 4-25.

(51) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 4 and at least one more ncRNA corresponding to SEQ ID NOs: 1-3, 5-25.

(52) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 5 and at least one more ncRNA corresponding to SEQ ID NOs: 1-4, 6-25.

(53) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 6 and at least one more ncRNA corresponding to SEQ ID NOs: 1-5, 7-25.

(54) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 7 and at least one more ncRNA corresponding to SEQ ID NOs: 1-6, 8-25.

(55) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 8 and at least one more ncRNA corresponding to SEQ ID NOs: 1-7, 9-25.

(56) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 9 and at least one more ncRNA corresponding to SEQ ID NOs: 1-8, 10-25.

(57) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 10 and at least one more ncRNA corresponding to SEQ ID NOs: 1-9, 11-25.

(58) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 11 and at least one more ncRNA corresponding to SEQ ID NOs: 1-10, 12-25.

(59) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 12 and at least one more ncRNA corresponding to SEQ ID NOs: 1-11, 13-25.

(60) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 13 and at least one more ncRNA corresponding to SEQ ID NOs: 1-12, 14-25.

(61) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 14 and at least one more ncRNA corresponding to SEQ ID NOs: 1-13, 15-25.

(62) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 15 and at least one more ncRNA corresponding to SEQ ID NOs: 1-14, 16-25.

(63) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 16 and at least one more ncRNA corresponding to SEQ ID NOs: 1-15, 17-25.

(64) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 17 and at least one more ncRNA corresponding to SEQ ID NOs: 1-16, 18-25.

(65) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 18 and at least one more ncRNA corresponding to SEQ ID NOs: 1-17, 19-25.

(66) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 19 and at least one more ncRNA corresponding to SEQ ID NOs: 1-18, 20-25.

(67) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 20 and at least one more ncRNA corresponding to SEQ ID NOs: 1-19, 21-25.

(68) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 21 and at least one more ncRNA corresponding to SEQ ID NOs: 1-20, 22-25.

(69) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 22 and at least one more ncRNA corresponding to SEQ ID NOs: 1-21, 23-25.

(70) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 23 and at least one more ncRNA corresponding to SEQ ID NOs: 1-22, 24-25.

(71) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 24 and at least one more ncRNA corresponding to SEQ ID NOs: 1-23, 25.

(72) In some embodiments, the methods of the present invention comprise measuring the level of the ncRNA corresponding to SEQ ID NO: 25 and at least one more ncRNA corresponding to SEQ ID NOs: 1-24.

(73) In some embodiments, the methods of the present invention comprise measuring the level of a plurality of ncRNAs corresponding to SEQ ID NO: 11 (microRNA 99b), SEQ ID NO: 14 (microRNA 151a), SEQ ID NO: 15 (microRNA 191), SEQ ID NO: 18 (microRNA 146b) and SEQ ID NO: 19 (microRNA 221), and optionally measuring the levels of at least one more ncRNA selected from ncRNAs corresponding to SEQ ID NOs: 1-10, 12-13, 16-17, 20-25.

(74) In some embodiments, the methods of the present invention comprise measuring the level of at least three, at least four or the five ncRNAs corresponding to SEQ ID NO: 11, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 18 and SEQ ID NO: 19, and optionally measuring the levels of at least one more ncRNA selected from ncRNAs corresponding to SEQ ID Nos: 1-10, 12-13, 16-17, 20-25.

(75) Non-coding RNAs having a sequence identity of at least 80-90% with the ncRNAs corresponding to SEQ ID Nos; 1-25 are also encompassed by the methods of the present invention.

(76) Reference Values/Patterns

(77) Preeclampsia and non-preeclampsia reference values are typically compiled from data obtained from a plurality of pregnant women. To generate the reference values, samples (e.g. plasma samples) are preferably taken from a large set of women during the first trimester of their pregnancy or up to week 14-15. Each woman is followed-up until the end of the pregnancy for preeclampsia development. Samples originating from women who did not develop preeclampsia during their pregnancy are classified as “non-preeclampsia” and the levels measured for the set of ncRNAs disclosed herein are stored as non-preeclampsia values. Samples originating from women that developed preeclampsia are classified as “preeclampsia” and the levels measured for the set of ncRNAs disclosed herein are stored as preeclampsia values.

(78) The data from the large set of women is analyzed to generate reference values (reference pattern) for preeclampsia, non-preeclampsia or both. Reference values may be represented in a number of ways. For example, reference values may be statistic values, such as mean values determined in each group. As another example, reference values may be threshold, or cutoff, values, differentiating between preeclampsia and non-preeclampsia.

(79) Thus, in some embodiments, preeclampsia reference pattern and/or preeclampsia reference values are determined from expression levels of the different ncRNAs in a subset of body fluid samples of pregnant women who developed preeclampsia out of a set of plasma samples of pregnant women collected at the first trimester of the pregnancy.

(80) When comparing an expression pattern of a tested sample to a reference pattern, for example to a preeclampsia pattern, each of the ncRNAs that are increased in preeclampsia may be indeed detected at elevated levels, but in some embodiments only a portion of the ncRNAs that are increased in preeclampsia is elevated in the tested sample. A pattern analyzing algorithm is typically used in order to determine whether an expression pattern of a tested sample as a whole correlates, or is different from, a reference pattern.

(81) In some embodiment, comparing comprises determining for each ncRNA if its expression level is above or below a predetermined cutoff that differentiates preeclampsia from non-preeclampsia. In some embodiments, detecting an expression level of a ncRNA above a preeclampsia cutoff identifies this ncRNA as representing preeclampsia or risk of preeclampsia. In some embodiments, comparing comprises allocating a score for each ncRNA identified as representing preeclampsia or risk of preeclampsia. In some embodiments, comparing comprises calculating a total score for each sample based on the number of ncRNAs identified as representing preeclampsia, and characterizing the woman as having preeclampsia wherein said total score is above a predefined threshold.

(82) Example of a score-based classification method can be carried out as follows: (i) For each marker, find the best split point to minimize the number of misclassified subjects. The split point defines two intervals: one for normal pregnancy and another for pregnancy destined to be complicated by preeclampsia. A score of 0 is assigned to a subject if its related observation falls in the normal interval; otherwise, a score of 1 is assigned. (ii) Overall, a subject is assigned a score as the sum of these assigned scores from m different markers. Therefore, the range of such score is between 0 and m. (iii) A given threshold (t) is used to predict the disease status for a given subject, e.g., a given woman with a total score equal or less than t is predicted to have normal status (e.g., pregnancy not destined to be complicated by preeclampsia), whereas a woman with a score higher than t will be diagnosed to have disease or likely to develop a disease (e.g., pregnancy destined to be complicated by preeclampsia).

(83) Typically, determining that an expression level is “different” from that of a reference level, for example increased or decreased compared to a reference level, indicates a statistically significant difference, for example a statistically significant increase or a statistically significant decrease compared to the reference level. As used herein, the term “statistically significant difference”, or simply “significant difference”, is well within the knowledge of a skilled artisan and can be determined empirically with reference to each particular biomarker or panel of biomarkers. For example, a significant difference in the body fluid level of a biomarker in a woman at risk of developing preeclampsia as compared to one not at risk of developing preeclampsia is any difference that is statistically significant (for example, level=p<0.05).

(84) Advantageously, the methods of the invention may employ the use of learning and pattern recognition analyzers, clustering algorithms and the like, in order to discriminate between expression patterns of samples obtained from pregnant woman that developed preeclampsia to control samples.

(85) The following examples are presented in order to more fully illustrate certain embodiments of the invention. They should in no way, however, be construed as limiting the broad scope of the invention. One skilled in the art can readily devise many variations and modifications of the principles disclosed herein without departing from the scope of the invention.

EXAMPLES

Example 1—Detecting Differentially Expressed ncRNAs Between Preeclampsia and Control Samples

(86) Methods: Non-coding RNA extraction and sequencing. Total RNA was extracted from plasma samples via miRNeasy™ Serum/Plasma Kit, and quantified using a NanoDrop spectrophotometer (ND-1000). The spectrophotometric absorbance parameters of the samples were: 260/280 nm˜1.8 and 260/230 nm˜1.8. Small RNA libraries were prepared for deep sequencing using Illumina's TruSeq® small RNA sample preparation kit. During this process, RNA molecules from each sample were ligated with 3′ and 5′ adapters, reverse-transcribed and then amplified using a PCR. Libraries of cDNA were prepared from 140-160 bp PCR products (representing 20-50 nt RNA molecules) and sequenced in separate lanes on an Illumina HiSeq® 2500 instrument. The plasma samples were processed in three batches (i.e., RNA extraction, library preparation and sequencing). Sequence reads profiling and differential expression analysis. Sequence reads were analyzed as follows: 1. Fastq-mcf tool was used for adapter sequences clipping, low quality (i.e., quality 30) bases trimming and filtering out short reads (i.e., reads with less than 16 nt). 2. Reads were mapped against Ensembl database for human ncRNAs (Flicek et al., 2014, Nucleic Acids Res. 42:D749-D755) using Burrows—Wheeler transform based alignment tool (BWA) (Li and Durbin, 2009, Bioinformatics 25:1754-1760). 3. Read counts were summarized in a counts matrix that contained the number of reads mapped to each transcript in each sample. Only uniquely mapped reads with no mismatches were considered. 4. Data normalization was performed using DESeq2 package in R to the effective library size (Love et al., 2014, Genome Biol. 15(12):550). Each column was divided by the geometric means of the rows. The median of these ratios (skipping the genes with a geometric mean of zero) was used as the size factor for this column. Each column of the count table was divided by the size factor for this column. This normalization brings all samples to a common scale and allows the comparison between samples. 5. Principal component analysis (PCA) and samples clustering were performed in R using prcomp and heatmap2 methods respectively to discover batch effects and outlier samples. 6. Differential expression analysis: DESeq2 (in R) was applied to obtain a list of differentially expressed transcripts between control and preeclampsia samples. Only transcripts with p-value<=0.05 after false discovery rate (FDR) adjustment were considered.

(87) Results:

(88) RNA was extracted and non-coding RNAs (mainly small ncRNAs but also degradation products of long ncRNAs) were sequenced from plasma samples taken from 75 pregnant women at the end of the first trimester: 35 women that developed preeclampsia (these women showed no signs of preeclampsia at the time of collecting the sample but developed preeclampsia afterwards) and 40 healthy women that did not develop preeclampsia during their pregnancy (a control set).

(89) Sequence reads were analyzed to obtain a counts matrix. In the matrix, a cell in the i.sup.th row and the j.sup.th column holds the number of reads mapped uniquely to gene i in sample j. As noted above, the samples were processed in three batches (i.e., RNA extraction, library preparation and sequencing), which inserted technical noise to the results. To examine the batch effect, principal component analysis (PCA) was performed. FIG. 1A shows the PCA plot displaying a clear batch effect that is captured in the first principal component. To remove this unwanted effect, ComBat method from sva package in R was used, which adjusts for known batches using an empirical Bayesian framework (Leek et al, 2012, Bioinformatics 28:882-883; and Johnson et al., 2007, Biostat. Oxf. Engl. 8:118-127). FIG. 1B shows the PCA plot after batch effect removal. The PCA plot also indicates the existence of two outlier samples (marked with a circle), which were removed from downstream analysis.

(90) Next, each transcript from the 100 most highly abundant transcripts was tested for differential expression in the preeclampsia/control group via DESeq2 package in R (Love et al., ibid). Twenty-five (25) transcripts were found to be differentially expressed between preeclampsia and control samples after correction for multiple testing (adjusted p-value<0.05, see Table 1): 16 transcripts were found to be up-regulated and 9 were down-regulated in preeclampsia compared to control. Of these, 7 transcripts were transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs) encoded in the mitochondria, 12 transcripts were microRNAs, 4 transcripts were long non-coding RNAs (linc), one transcript was ribosomal RNA and one transcript was processed transcript (i.e., a non-coding transcript that does not belong to any of the categories in Ensembl database).

(91) TABLE-US-00001 TABLE 1 Differentially expressed ncRNAs in preeclampsia vs. control samples *Base mean **Fold Adjusted Transcript ID.sup.a Description count change P-value P-value ENST00000387461 mitochondrially 489.86 4.25 1.65E−16 1.57E−14 (SEQ ID NO: 1) encoded tRNA proline ENST00000387421 mitochondrially 433.25 2.27 3.43E−06 1.63E−04 (SEQ ID NO: 2) encoded tRNA lysine ENST00000385255 microRNA 182 1325.43 0.54 5.45E−06 1.73E−04 (SEQ ID NO: 3) ENST00000385011 microRNA 10b 7115.29 0.50 8.96E−06 2.13E−04 (SEQ ID NO: 4) ENST00000361558 mucin 2, 900.99 2.34 1.68E−05 3.19E−04 (SEQ ID NO: 5) oligomeric mucus/gel- forming (processed transcript) ENST00000384816 microRNA 25 5584.66 0.61 5.38E−05 6.39E−04 (SEQ ID NO: 6) ENST00000514519 RP11-259O2.3-001 408.71 2.97 4.92E−05 6.39E−04 (SEQ ID NO: 7) (lincRNA) ENST00000581329 microRNA 4433b 473.02 1.71 4.98E−05 6.39E−04 (SEQ ID NO: 8) ENST00000387441 mitochondrially 246.72 1.95 9.21E−05 9.72E−04 (SEQ ID NO: 9) encoded tRNA histidine ENST00000626826 HELLP 729.26 2.02 1.08E−04 1.03E−03 (SEQ ID NO: 10) associated long non-coding RNA (macro_lncRNA) ENST00000384819 microRNA 99b 344.45 0.65 1.57E−04 1.31E−03 (SEQ ID NO: 11) ENST00000385300 microRNA 143 1631.94 0.62 1.66E−04 1.31E−03 (SEQ ID NO: 12) ENST00000387342 mitochondrially 664.18 1.99 2.11E−04 1.54E−03 (SEQ ID NO: 13) encoded tRNA valine ENST00000521276 microRNA 151a 10020.95 0.75 5.68E−04 3.85E−03 (SEQ ID NO: 14) ENST00000384873 microRNA 191 31187.05 0.75 6.26E−04 3.97E−03 (SEQ ID NO: 15) ENST00000365096 RNA, 5.8S 1652.37 1.68 1.65E−03 9.21E−03 (SEQ ID NO: 16) ribosomal pseudogene 4 (rRNA) ENST00000387449 mitochondrially 1249.73 1.72 1.61E−03 9.21E−03 (SEQ ID NO: 17) encoded tRNA serine 2 (AGU/C) ENST00000365699 microRNA 146b 1322.69 0.75 2.67E−03 1.41E−02 (SEQ ID NO: 18) ENST00000385135 microRNA 221 587.31 1.44 3.97E−03 1.98E−02 (SEQ ID NO: 19) ENST00000387409 mitochondrially 173.20 1.53 4.41E−03 2.09E−02 (SEQ ID NO: 20) encoded tRNA tyrosine ENST00000387347 mitochondrially 6217.70 1.63 4.82E−03 2.18E−02 (SEQ ID NO: 21) encoded 16S RNA ENST00000362280 microRNA let-7g 1072.71 1.27 9.85E−03 4.26E−02 (SEQ ID NO: 22) ENST00000315707 long intergenic 1086.88 1.50 1.03E−02 4.27E−02 (SEQ ID NO: 23) non-protein coding RNA 324 (lincRNA) ENST00000612171 AC113133.1-201 17568.81 0.70 1.11E−02 4.38E−02 (SEQ ID NO: 24) (miRNA-486) ENST00000614316 AC020956.3-001 901.99 1.71 1.18E−02 4.47E−02 (SEQ ID NO: 25) (lincRNA) .sup.aThe transcript ID is the accession number in Ensembl database. The sequences are cDNA sequences corresponding to the RNA transcripts. *mean of normalized counts for all samples **preeclampsia compared to control

(92) FIG. 2 demonstrates the count differences in both conditions (preeclampsia—“PE”, and control) for the significant differentially expressed transcripts listed in Table 1.

(93) Next, the correlation of each of the differentially expressed transcripts and maternal clinical features (as measured during the first trimester, at the time of taking the blood sample for the RNA analyses) was analyzed. The analysis has shown moderate yet significant correlations between miR-4433b (SEQ ID NO: 8) and two maternal clinical features (FIGS. 3A+B): the uterine artery pulsatility index (UT PI; r=0.395, adjusted p-value=0.016), and the mean arterial pressure (MAP; r=0.442, adjusted p-value=0.003).

(94) In order to validate the expression data obtained by sequencing, the expression of five microRNAs was examined using qPCR in 14 samples: 6 preeclampsia samples and 8 control samples, and the correlation between the expression of these microRNAs obtained by sequencing and by qPCR in the relevant samples was analyzed. Pearson's product moment correlation coefficients were calculated between normalized counts obtained by sequencing and qPCR normalized values (2.sup.−Ct). The analysis has shown a significant correlation for all tested microRNAs (see Table 2), which confirms the microRNA counts obtained by sequencing.

(95) TABLE-US-00002 TABLE 2 Expression of microRNAs - sequencing vs. qPCR Correlation Fold between sequencing miRNA change P-value and qPCR (r.sup.2) P-value microRNA 99b 0.65 1.57E−04 0.83 0.017 (SEQ ID NO: 11) microRNA 151a 0.75 5.68E−04 0.73 0.011 (SEQ ID NO: 14) microRNA 191 0.75 6.26E−04 0.7 0.016 (SEQ ID NO: 15) microRNA 146b 0.75 2.67E−03 0.85 0.00099 (SEQ ID NO: 18) microRNA 221 1.44 3.97E−03 0.83 0.0015 (SEQ ID NO: 19)

Example 2—Preeclampsia/Control Sample Classification

(96) Methods:

(97) As noted above, counts were normalized and transformed using DESeq2 by normalization to the effective library size and variance stabilizing transformations (VST) to estimate the mean-dispersion relationship of data (Love et al., ibid). Next, for the purpose of building a generalized classifier and estimating its performance when applied on new samples, logistic regression in a k-fold cross validation (CV) procedure was used.

(98) In order to obtain more generalizable models, the cross validation concept was applied 100 times (cycles). In each cycle, the data was divided to training and test sets, and 5-fold cross validation was applied on the training set, i.e., the training set was divided to 5 non-overlapping and equally sized subsets, a logistic regression model was trained on 4 subsets and tested in the remaining subset. This process was repeated 5 times, thus all subsets were used as a test set in each step. For model selection glmulti package in R was used (Calcagno and de Mazancourt, 2010, J. Stat. Softw. 34:29), which performs an exhaustive search over all possible models, fit each model to the current set using glm and ranks them by Akaike information criterion (AIC). A feature selection procedure was applied in each CV cycle and the list of transcripts was narrowed down to differentially expressed transcripts that have substantial expression (i.e., in the top 100 most abundant transcripts). Highly correlated transcripts (i.e., Pearson correlation >0.7) were then collapsed. Best model (i.e., with the highest AIC calculated by glmulti) was tested in each CV step on the reaming subset (i.e., the inner test set), and misclassification error was calculated. The model that got the lowest CV error in all CV steps was then selected, fitted on the complete training set and tested on the outer test set. After 100 repeats, the average error rate and related statistics was calculated.

(99) Results:

(100) K-fold cross validation was applied in 100 repeats to obtain a generalizable logistic regression model that includes feature selection pre-processing step. Using this technique, the goodness of the procedure was estimated on a new blind data set. FIG. 4 displays summary statistic histograms, mean and medians calculated in each of the 100 repeats. All measures imply that the procedure can classify blind samples with preeclampsia/control conditions in a relatively good manner.

Example 3—Expression of the Differentially Expressed Transcripts Over Trimesters

(101) To assess the expression of the 25 differentially expressed transcripts later in the pregnancy, after the appearance of preeclampsia symptoms, small ncRNAs were sequenced in the plasma of a subset of 40 women (out of the 75) at weeks 20-22 (second trimester): 20 women who developed preeclampsia and 20 control women. The sequencing data from the second trimester samples were compared to those of the first trimester samples.

(102) Before the comparison was made, the first trimester samples of this subset of 40 women were analyzed again. As this subset is half the size of the original size and thus insufficiently powered, out of the 25 differentially expressed transcripts, only 9 transcripts were significantly differentially expressed between preeclampsia and control samples in the limited set of 40 first trimester samples (adjusted p-value<0.05). In the second trimester samples, 4 transcripts out of the 25 were differentially expressed (adjusted p-value<0.05).

(103) The first and second trimester samples were then combined and a design formula was used, which models the condition-specific (i.e., preeclampsia or control) difference at the first trimester, the difference over trimesters, and any condition-specific differences over trimesters (i.e., an interaction term condition:trimester). A likelihood ratio test was performed with a reduced model which does not contain the interaction term, to test whether the condition induces a change in gene expression at the second trimester compared to the first trimester. Only transcripts with p-value<0.05 after false discovery rate (FDR) adjustment were considered. Applying this test, none of the transcripts displayed significant preeclampsia-dependent change in expression over the trimesters (preeclampsia/trimester interaction test, see Table 3 and FIG. 5. These results suggest that fold changes in expression observed in the first trimester are maintained during the second trimester as well, after the appearance of preeclampsia symptoms, though more samples from both gestational ages are required to further investigate this matter.

(104) TABLE-US-00003 TABLE 3 Comparison of first and second trimesters Base mean *Fold Adjusted Transcript ID Description count change P-value P-value ENST00000521276 microRNA 151a 4,514.08 0.27 0.04 0.40 (SEQ ID NO: 14) ENST00000384873 microRNA 191 12,546.19 0.28 0.06 0.40 (SEQ ID NO: 15) ENST00000384819 microRNA 99b 129.85 0.31 0.07 0.40 (SEQ ID NO: 11) ENST00000362280 microRNA let-7g 653.84 0.36 0.08 0.40 (SEQ ID NO: 22) ENST00000387342 mitochondrially 1,517.16 0.31 0.09 0.40 (SEQ ID NO: 13) encoded tRNA valine ENST00000385135 microRNA 221 446.48 0.36 0.10 0.40 (SEQ ID NO: 19) ENST00000626826 HELLP 497.67 1.30 0.14 0.49 (SEQ ID NO: 10) associated long non- coding RNA ENST00000365699 microRNA 146b 413.93 0.42 0.17 0.52 (SEQ ID NO: 18) ENST00000385300 microRNA 143 861.77 0.45 0.19 0.52 (SEQ ID NO: 12) ENST00000385255 microRNA 182 617.00 0.47 0.23 0.52 (SEQ ID NO: 3) ENST00000612171 AC113133.1-201 10,420.59 0.48 0.24 0.52 (SEQ ID NO: 24) ENST00000387461 mitochondrially 315.89 1.74 0.25 0.52 (SEQ ID NO: 1) encoded tRNA proline ENST00000385011 microRNA 10b 2,488.02 0.53 0.27 0.53 (SEQ ID NO: 4) ENST00000514519 RP11-259O2.3-001 209.70 0.60 0.39 0.63 (SEQ ID NO: 7) ENST00000384816 microRNA 25 2,728.09 0.59 0.40 0.63 (SEQ ID NO: 6) ENST00000387441 mitochondrially 356.59 1.70 0.40 0.63 (SEQ ID NO: 9) encoded tRNA histidine ENST00000387449 mitochondrially 1,106.02 1.44 0.52 0.74 (SEQ ID NO: 17) encoded tRNA serine 2 (AGU/C) ENST00000614316 AC020956.3-001 568.39 0.80 0.54 0.74 (SEQ ID NO: 25) ENST00000315707 long intergenic 651.28 0.83 0.57 0.74 (SEQ ID NO: 23) non-protein coding RNA 324 ENST00000387421 mitochondrially 926.67 0.71 0.60 0.74 (SEQ ID NO: 2) encoded tRNA lysine ENST00000387409 mitochondrially 207.66 1.27 0.64 0.74 (SEQ ID NO: 20) encoded tRNA tyrosine ENST00000387347 mitochondrially 6,306.61 1.28 0.68 0.74 (SEQ ID NO: 21) encoded 16S RNA ENST00000365096 RNA, 5.8S 1,782.07 0.81 0.69 0.74 (SEQ ID NO: 16) ribosomal pseudogene 4 ENST00000581329 microRNA 4433b 200.30 0.79 0.72 0.75 (SEQ ID NO: 8) ENST00000361558 mucin 2, 792.728 0.94 0.87 0.87 (SEQ ID NO: 5) oligomeric mucus/gel- forming *fold changes are a ratio of first trimester fold change divided by second trimester fold change.

Example 4—Expression of the Differentially Expressed Transcripts in the Placenta

(105) Although the cause of preeclampsia is unclear, it is known that it involves inadequate blood supply to the placenta, leading to a hypoxic environment. To evaluate the effect of hypoxic conditions on the expression level of the differentially expressed transcripts, four (4) microRNAs out of the 25 differentially expressed transcripts were tested for their expression in human first-trimester cytotrophoblast cell line (HTR8/SVneo cells) cultured for 48 hours under hypoxic or normal conditions. Out of the four tested microRNAs, 2 microRNAs, miR-10b and miR-221, displayed significant differential expression, similar to the differential expression identified in the plasma (FIG. 6). These results suggest that their observed expression changes in the plasma originated in the placenta.

(106) To further inspect this matter, the expression of the 25 differentially expressed transcripts was tested in preeclampsia and control placenta biopsies. Placenta segments were collected from 15 different locations in the placenta, from 6 different placentas: 3 from preeclampsia patients and 3 from healthy pregnant women. Small ncRNA from all locations was extracted and sequenced, and the expression of all 25 transcripts was examined. In this sample set, none of the transcripts displayed differential expression in those samples between preeclampsia and control groups.

(107) The foregoing description of the specific embodiments will so fully reveal the general nature of the invention that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without undue experimentation and without departing from the generic concept, and therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. The means, materials, and steps for carrying out various disclosed chemical structures and functions may take a variety of alternative forms without departing from the invention.