A preeclampsia-specific circulating RNA signature

Abstract

To provide, e.g., methods and materials for use in detecting preeclampsia and / or determining an increased risk for preeclampsia in a pregnant female.SOLUTION: The method includes identifying a plurality of circulating RNA (C-RNA) molecules in a biosample collected from the pregnant female.SELECTED DRAWING: None

Description

[Technical field]

[0001] Continuing Application Data This application claims the benefit of U.S. Provisional Patent Application No. 62 / 676,436, filed May 25, 2018, and U.S. Provisional Patent Application No. 62 / 848,219, filed May 15, 2019, each of which is incorporated by reference in its entirety.

[0002] The present invention relates generally to methods and materials for use in the detection and early risk assessment of the pregnancy complication preeclampsia. [Background technology]

[0003] Preeclampsia is a disease that occurs only during pregnancy and affects 5% to 8% of all pregnancies. Preeclampsia is the direct cause of 10% to 15% of maternal deaths and 40% of fetal deaths. The three main symptoms of preeclampsia include high blood pressure, swelling of the hands and feet, and excess protein in the urine (proteinuria), which occur after the 20th week of pregnancy. Other signs and symptoms of preeclampsia include severe headaches, vision changes (including temporary loss of vision, blurred vision, or sensitivity to light), nausea or vomiting, decreased urine output, decreased platelet counts (thrombocytopenia), liver dysfunction, and shortness of breath due to fluid in the lungs.

[0004] The more severe preeclampsia is and the earlier it occurs in pregnancy, the higher the risks for the mother and baby. Preeclampsia may require induced labor or delivery by Caesarean section. Left untreated, preeclampsia can cause serious, even life-threatening complications for both the mother and baby. Complications of preeclampsia include fetal growth restriction, low birth weight, premature birth, placental abruption, HELLP syndrome (hemolysis, elevated liver enzymes, and low platelet count syndrome), eclampsia (a severe form of preeclampsia that causes seizures), organ damage (including kidney, liver, lung, heart, or eye damage), stroke or other brain damage. See, for example, Mayoclinic.org / diseases-conditions / preeclampsia / symptoms-causes / syc-20355745 (Eds. 2002).

[0005] With early detection and treatment, most women can give birth to healthy children if preeclampsia is detected early and treated at regular prenatal checkups. Although various protein biomarkers show altered levels in maternal serum at presymptomatic stages, these biomarkers lack the ability to differentiate and predict in individual patients (Non-Patent Document 2). Therefore, the identification of biomarkers for early detection of preeclampsia is important for early diagnosis and treatment of preeclampsia. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] “Preeclampsia-Symptoms and causes-Mayo Clinic,”April 3,2018 [Non-Patent Document 2] Karumanchi and Granger,2016,Hypertension;67(2):238-242 Summary of the Invention [Means for solving the problem]

[0007] The present invention relates to a method for detecting preeclampsia and / or determining an increased risk of preeclampsia in a pregnant woman, comprising the steps of: identifying a plurality of circulating RNA (C-RNA) molecules in a biological sample obtained from the pregnant woman; Here, multiple C-RNA molecules are (a) ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1, T MEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L 6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, PARN, ORAI3, ELMO3, S Any one or more of MPD3, SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3, any two or more of them, any three or more of them, any four or more of them, any five or more of them, any six or more of them, any seven or more of them, any eight or more of them, any nine or more of them, or any ten or more of them a plurality of C-RNA molecules encoding at least a portion of a protein selected from any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, up to a maximum of all 75 proteins; or (b) any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, or any seven of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, or all 27; or (c)CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3 , ALPK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L , CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37, ZEB1, JAG2, KIF26A, APOLD1, P NMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, P RX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMP5, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KR Any one or more of T5, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, any nine or more, any ten or more, any eleven or more, any twelve or more, any thirteen or more, any fourteen or more, any fifteen or more, any sixteen or more, any seventeen or more, any eighteen or more, any nineteen or more, any twenty or more, any twentyone or more, any twentytwo or more, any twentythree or more, any twentyfour or more, any twentyfive or more,A plurality of C-RNA molecules encoding at least a portion of a protein selected from all up to 122; or (d) any one or more of VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4, any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, or any a plurality of C-RNA molecules encoding at least a portion of proteins selected from any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, any 27 or more, any 28 or more, any 29 or more, or all 30; or (e) any one or more, any two or more, any three or more, any four or more, any five or more, any six or more of ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 a plurality of C-RNA molecules encoding at least a portion of a protein selected from any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, or all 26; or (f) any one or more, any two or more, any three or more, any four or more, any five or more, or any of the following: ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 a plurality of C-RNA molecules encoding at least a portion of a protein selected from 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, or all 22; or (g) any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, any nine or more, any ten or more, or all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES (in some embodiments, seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, a plurality of C-RNA molecules encoding at least a portion of a protein selected from: eight of ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ten of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; six of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or eight of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL; or (h) any one or more, any two or more, any three or more, any four or more, any five or more, any six or more of LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1 A plurality of C-RNA molecules encoding at least a portion of a protein selected from any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, or all 24. is selected from The present invention includes methods that indicate preeclampsia and / or an increased risk of preeclampsia in a pregnant woman.

[0008] The present invention relates to a method for detecting preeclampsia and / or determining an increased risk of preeclampsia in a pregnant woman, comprising the steps of: Obtaining a biological sample from a pregnant woman; purifying a population of circulating RNA (C-RNA) molecules from a biological sample; identifying a protein coding sequence encoded by a C-RNA molecule within the purified population of C-RNA molecules; A protein coding sequence encoded by a C-RNA molecule encoding at least a portion of a protein, (a) ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX 2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, AST E1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2, M TRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, PARN, ORAI3 , ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3, any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, or any nine or more , any 10 or more, any 11 or more, any 12, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 50 or more, any 70 or more, or all 75; or (b) any one or more, any two or more, any three or more, any four or more, or any five of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, or all 27; or (c)CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FST L3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, ALPK 3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L, CA3, HRG , TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3, any one or more of PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMP5, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5; 2 or more of any, 3 or more of any, 4 or more of any, 5 or more of any, 6 or more of any, 7 or more of any, 8 or more of any, 9 or more of any, 10 or more of any, 11 or more of any, 12 or more of any, 13 or more of any, 14 or more of any, 15 or more of any, 16 or more of any, 17 or more of any, 18 or more of any, 19 or more of any, 20 or more of any, 21 or more of any, 22 or more of any, 23 or more of any, 24 or more of any, 25 or more of any, 50 or more of any, 75 or more of any,Any 100 or more, or any 122; or (d) any one or more, any two or more, any three or more, any four or more, any five or more, or none of the following: VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4 6 or more of any of them, 7 or more of any of them, 8 or more of any of them, 9 or more of any of them, 10 or more of any of them, 11 or more of any of them, 12 or more of any of them, 13 or more of any of them, 14 or more of any of them, 15 or more of any of them, 16 or more of any of them, 17 or more of any of them, 18 or more of any of them, 19 or more of any of them, 20 or more of any of them, 21 or more of any of them, 22 or more of any of them, 23 or more of any of them, 24 or more of any of them, 25 or more of any of them, 26 or more of any of them, 27 or more of any of them, 28 or more of any of them, 29 or more of any of them, or all 30; or (e) any one or more, any two or more, any three or more, or any four or more of ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 5 or more of any, 6 or more of any, 7 or more of any, 8 or more of any, 9 or more of any, 10 or more of any, 11 or more of any, 12 or more of any, 13 or more of any, 14 or more of any, 15 or more of any, 16 or more of any, 17 or more of any, 18 or more of any, 19 or more of any, 20 or more of any, 21 or more of any, 22 or more of any, 23 or more of any, 24 or more of any, 25 or more of any, or all 26; or (f) any one or more, any two or more, any three or more, or any of ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, or all 22; or (g) any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, any nine or more, any ten or more, or all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES (in some embodiments, seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CL eight of EC4C, LEP, PAPPA2, SKIL, and VSIG4; eight of ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ten of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; six of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or eight of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL); or (h) any one or more, any two or more, any three or more, or any four or more of LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1 5 or more of any, 6 or more of any, 7 or more of any, 8 or more of any, 9 or more of any, 10 or more of any, 11 or more of any, 12 or more of any, 13 or more of any, 14 or more of any, 15 or more of any, 16 or more of any, 17 or more of any, 18 or more of any, 19 or more of any, 20 or more of any, 21 or more of any, 22 or more of any, 23 or more of any, or all 24 is selected from The present invention includes methods that indicate preeclampsia and / or an increased risk of preeclampsia in a pregnant woman.

[0009] In some embodiments, identifying the protein-coding sequence encoded by the C-RNA molecule in the biological sample comprises hybridization, reverse transcription PCR, microarray chip analysis, or sequencing.

[0010] In some embodiments, identifying protein-coding sequences encoded by C-RNA molecules within a biological sample comprises sequencing, including, for example, massively parallel sequencing and / or RNA sequencing of clonally amplified molecules.

[0011] In some embodiments, the method further comprises removing intact cells from the biological sample prior to identifying protein-coding sequences encoded by the circular RNA (C-RNA) molecules; treating the biological sample with deoxynuclease (DNase) to remove cell-free DNA (cfDNA); synthesizing complementary DNA (cDNA) from the C-RNA molecules in the biological sample; and / or enriching the cDNA sequences for protein-coding DNA sequences by exome enrichment.

[0012] The present invention relates to a method for detecting preeclampsia and / or determining an increased risk of preeclampsia in a pregnant woman, comprising the steps of: Obtaining a biological sample from a pregnant woman; removing intact cells from the biological sample; Treating the biological sample with deoxynuclease (DNase) to remove cell-free DNA (cfDNA); synthesizing complementary DNA (cDNA) from the RNA molecules in the biological sample; enriching cDNA sequences for protein-coding DNA sequences (exome enrichment); sequencing the resulting enriched cDNA sequences; identifying protein coding sequences encoded by the enriched C-RNA molecules; Includes; (a) ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, C UX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG 1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, Any one or more of the following genes: PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3; any two or more of them; any three or more of them; any four or more of them; any five or more of them; any six or more of them; any seven or more of them; 8 or more of any of them, 9 or more of any of them, 10 or more of any of them, 11 or more of any of them, 12 or more of any of them, 13 or more of any of them, 14 or more of any of them, 15 or more of any of them, 16 or more of any of them, 17 or more of any of them, 18 or more of any of them, 19 or more of any of them, 20 or more of any of them, 21 or more of any of them, 22 or more of any of them, 23 or more of any of them, 24 or more of any of them, 25 or more of any of them, up to a maximum of 75; or (b) any one or more, any two or more, any three or more, any four or more, or any five of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, or all 27; or (c)CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FS TL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, AL PK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L, CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYO Any of M3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMP5, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5 1 or more, any 2 or more, any 3 or more, any 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, up to a maximum of 122; or (d) any one or more, any two or more, any three or more, any four or more, any five or more, or none of the following: VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4 6 or more of any of them, 7 or more of any of them, 8 or more of any of them, 9 or more of any of them, 10 or more of any of them, 11 or more of any of them, 12 or more of any of them, 13 or more of any of them, 14 or more of any of them, 15 or more of any of them, 16 or more of any of them, 17 or more of any of them, 18 or more of any of them, 19 or more of any of them, 20 or more of any of them, 21 or more of any of them, 22 or more of any of them, 23 or more of any of them, 24 or more of any of them, 25 or more of any of them, 26 or more of any of them, 27 or more of any of them, 28 or more of any of them, 29 or more of any of them, or all 30; or (e) any one or more, any two or more, any three or more, or any four or more of ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 5 or more of any, 6 or more of any, 7 or more of any, 8 or more of any, 9 or more of any, 10 or more of any, 11 or more of any, 12 or more of any, 13 or more of any, 14 or more of any, 15 or more of any, 16 or more of any, 17 or more of any, 18 or more of any, 19 or more of any, 20 or more of any, 21 or more of any, 22 or more of any, 23 or more of any, 24 or more of any, 25 or more of any, or all 26; or (f) any one or more, any two or more, any three or more, or any of ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, or all 22; or (g) any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, any nine or more, any ten or more, or all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES (in some embodiments, seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CL eight of EC4C, LEP, PAPPA2, SKIL, and VSIG4; eight of ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ten of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; six of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or eight of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL); or (h) any one or more, any two or more, any three or more, or any four or more of LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1 5 or more of any, 6 or more of any, 7 or more of any, 8 or more of any, 9 or more of any, 10 or more of any, 11 or more of any, 12 or more of any, 13 or more of any, 14 or more of any, 15 or more of any, 16 or more of any, 17 or more of any, 18 or more of any, 19 or more of any, 20 or more of any, 21 or more of any, 22 or more of any, 23 or more of any, or all 24 wherein a protein coding sequence encoded by a C-RNA molecule encoding at least a portion of a protein selected from the group consisting of is indicative of preeclampsia and / or an increased risk of preeclampsia in a pregnant woman.

[0013] The present invention includes a method for identifying circulating RNA signatures associated with an increased risk of preeclampsia, comprising the steps of: obtaining a biological sample from a pregnant woman; removing intact cells from the biological sample; treating the biological sample with deoxynuclease (DNase) to remove cell-free DNA (cfDNA); synthesizing complementary DNA (cDNA) from RNA molecules in the biological sample; enriching the cDNA sequences for protein-coding DNA sequences (exome enrichment); sequencing the resulting enriched cDNA sequences; and identifying protein-coding sequences encoded by the enriched C-RNA molecules.

[0014] The present invention relates to Obtaining a biological sample from a pregnant woman; removing intact cells from the biological sample; Treating the biological sample with deoxynuclease (DNase) to remove cell-free DNA (cfDNA); synthesizing complementary DNA (cDNA) from the RNA molecules in the biological sample; enriching cDNA sequences for protein-coding DNA sequences (exome enrichment); sequencing the resulting enriched cDNA sequences; identifying protein coding sequences encoded by the enriched C-RNA molecules; 1. A method comprising: wherein the protein coding sequence is (a) ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HE G1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5 , HIPK2, MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS , PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3, any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25, up to a maximum of 75; or (b) any one or more, any two or more, any three or more, any four or more, or any five of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, or all 27; or (c)CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FS TL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, AL PK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L, CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MY any of OM3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMP5, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5 or 1 or more, any 2 or more, any 3 or more, any 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, up to a maximum of 122; or (d) any one or more, any two or more, any three or more, any four or more, any five or more, or none of the following: VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4 6 or more of any of them, 7 or more of any of them, 8 or more of any of them, 9 or more of any of them, 10 or more of any of them, 11 or more of any of them, 12 or more of any of them, 13 or more of any of them, 14 or more of any of them, 15 or more of any of them, 16 or more of any of them, 17 or more of any of them, 18 or more of any of them, 19 or more of any of them, 20 or more of any of them, 21 or more of any of them, 22 or more of any of them, 23 or more of any of them, 24 or more of any of them, 25 or more of any of them, 26 or more of any of them, 27 or more of any of them, 28 or more of any of them, 29 or more of any of them, or all 30; or (e) any one or more, any two or more, any three or more, or any four or more of ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 5 or more of any, 6 or more of any, 7 or more of any, 8 or more of any, 9 or more of any, 10 or more of any, 11 or more of any, 12 or more of any, 13 or more of any, 14 or more of any, 15 or more of any, 16 or more of any, 17 or more of any, 18 or more of any, 19 or more of any, 20 or more of any, 21 or more of any, 22 or more of any, 23 or more of any, 24 or more of any, 25 or more of any, or all 26; or (f) any one or more, any two or more, any three or more, or any of ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, or all 22; or (g) any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, any nine or more, any ten or more, or all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES (in some embodiments, seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CL eight of EC4C, LEP, PAPPA2, SKIL, and VSIG4; eight of ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ten of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; six of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or eight of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL); or (h) any one or more, any two or more, any three or more, or any four or more of LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1 5 or more of any, 6 or more of any, 7 or more of any, 8 or more of any, 9 or more of any, 10 or more of any, 11 or more of any, 12 or more of any, 13 or more of any, 14 or more of any, 15 or more of any, 16 or more of any, 17 or more of any, 18 or more of any, 19 or more of any, 20 or more of any, 21 or more of any, 22 or more of any, 23 or more of any, or all 24 The present invention also includes a method comprising comprising at least a portion of a protein selected from the group consisting of:

[0015] In some embodiments, the biological sample comprises plasma.

[0016] In some embodiments, the biological sample is taken from a pregnant woman who is less than 16 weeks or less than 20 weeks pregnant.

[0017] In some embodiments, the biological sample is taken from a pregnant woman who is greater than 20 weeks pregnant.

[0018] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4 A3, TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GAT SL2, SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, P Any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, any nine or more, any ten or more of the following: ARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3 The C-RNA signatures include C-RNA signatures encoding at least a portion of any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 50 or more, any 70 or more, or up to a maximum of all 75 of the above.

[0019] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising any one or more, any two or more, any three or more, or any four of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4. or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, or all 27 of the C-RNA signatures.

[0020] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising multiple CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2, NF-kappaB1, NF-kappaB ... L1, PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, ALPK3, SAMD4 A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWT R1, AIF1L, CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14o rf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, AD and C-RNA signatures encoding at least a portion of AMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMP5, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5.

[0021] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising any one or more, any two or more, any three or more, any four or more, or any combination of VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4. and / or a C-RNA signature encoding at least a portion of any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, any 27 or more, any 28 or more, any 29 or more, or all 30 of the C-RNA signatures.

[0022] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising any one or more, any two or more, any three or more, or any combination of ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4. The C-RNA signatures include C-RNA signatures encoding at least a portion of any 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, or all 26 of the C-RNA signatures.

[0023] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising any one or more, any two or more, or any combination of ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4. The C-RNA signatures include those encoding at least a portion of 3 or more, any 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, or all 22 of the C-RNA signatures.

[0024] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising any one or more, any two or more, any three or more, any four or more, any five or more, any six or more, any seven or more, any eight or more, any nine or more, any ten or more, or all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES (in some embodiments, seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, eight of ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ten of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; six of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or eight of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL.

[0025] The present invention provides a circulating RNA (C-RNA) signature for increased risk of preeclampsia, comprising any one or more, any two or more, any three or more, or any combination of LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1. or 4 or more, any 5 or more, any 6 or more, any 7 or more, any 8 or more, any 9 or more, any 10 or more, any 11 or more, any 12 or more, any 13 or more, any 14 or more, any 15 or more, any 16 or more, any 17 or more, any 18 or more, any 19 or more, any 20 or more, any 21 or more, any 22 or more, any 23 or more, or all 24 of the C-RNA signatures.

[0026] The present invention includes solid support arrays comprising a plurality of agents capable of binding to and / or identifying the C-RNA signatures described herein.

[0027] The present invention includes kits comprising a plurality of probes capable of binding to and / or identifying the C-RNA signatures described herein.

[0028] The present invention includes a kit comprising a plurality of primers for selectively amplifying the C-RNA signatures described herein.

[0029] As used herein, the term "nucleic acid" is intended to be consistent with its use in the art and includes naturally occurring nucleic acids or functional analogs thereof. Particularly useful functional analogs can hybridize to nucleic acids in a sequence-specific manner or can be used as templates for replicating specific nucleotide sequences. Naturally occurring nucleic acids generally have a backbone containing phosphodiester bonds. Analog structures can have alternative backbone bonds, including any of a variety known in the art. Naturally occurring nucleic acids generally have a deoxyribose sugar (e.g., found in deoxyribonucleic acid (DNA)) or a ribose sugar (e.g., found in ribonucleic acid (RNA)). Nucleic acids can include any of the various analogs of these sugar moieties known in the art. Nucleic acids can include natural or unnatural bases. In this regard, natural deoxyribonucleic acids can have one or more bases selected from the group consisting of adenine, thymine, cytosine, or guanine, and ribonucleic acids can have one or more bases selected from the group consisting of uracil, adenine, cytosine, or guanine. Useful unnatural bases that can be included in nucleic acids are known in the art. The terms "template" and "target," when used in reference to a nucleic acid, are intended as semantic identifiers of the nucleic acid in the context of the methods or compositions described herein and do not necessarily limit the structure or function of the nucleic acid beyond that otherwise expressly indicated.

[0030] As used herein, "amplify", "amplification" or "amplification reaction" and their derivatives generally refer to any act or process in which at least a portion of a nucleic acid molecule is duplicated or copied to at least one additional nucleic acid molecule. The additional nucleic acid molecule optionally comprises a sequence that is substantially identical or substantially complementary to at least some portion of the target nucleic acid molecule. The target nucleic acid molecule may be single-stranded or double-stranded, and the additional nucleic acid molecule may be independently single-stranded or double-stranded. The amplification optionally comprises linear or exponential replication of the nucleic acid molecule. In some embodiments, such amplification may be performed using isothermal conditions, and in other embodiments, such amplification may comprise thermal cycling. In some embodiments, the amplification is a multiplex amplification that comprises simultaneous amplification of multiple target sequences in a single amplification reaction. In some embodiments, "amplification" comprises the amplification of at least some portions of DNA and RNA-based nucleic acids, alone or in combination. The amplification reaction may comprise any of the amplification processes known to those of skill in the art. In some embodiments, the amplification reaction comprises a polymerase chain reaction (PCR).

[0031] As used herein, "amplification conditions" and its derivatives generally refer to conditions suitable for amplifying one or more nucleic acid sequences. Such amplification may be linear or exponential. In some embodiments, amplification conditions may include isothermal conditions, or may include thermocycling conditions, or a combination of isothermal and thermocycling conditions. In some embodiments, conditions suitable for amplifying one or more nucleic acid sequences include polymerase chain reaction (PCR) conditions. Typically, amplification conditions refer to a reaction mixture sufficient to amplify a nucleic acid, such as one or more target sequences, or to amplify an amplification target sequence linked to one or more adapters, such as an adapter-ligated amplification target sequence. In general, amplification conditions include a catalyst for amplification or nucleic acid synthesis, such as a polymerase; a primer having a degree of complementarity to the nucleic acid to be amplified; and nucleotides, such as deoxyribonucleotide triphosphates (dNTPs), to facilitate extension of the primer after hybridization to the nucleic acid. Amplification conditions may require hybridization or annealing of the primer to the nucleic acid, extension of the primer, and a denaturation step in which the extended primer is separated from the nucleic acid sequence undergoing amplification. Typically, amplification conditions can, but do not necessarily, include thermal cycling, and in some embodiments, amplification conditions include multiple cycles in which the steps of annealing, extension, and separation are repeated. Typically, amplification conditions include multiple cycles in which the steps of annealing, extension, and separation are repeated. ++ or Mn ++ and may also include various adjusters of ionic strength.

[0032] As used herein, the term "polymerase chain reaction" (PCR) refers to the method of K.B. Mullis U.S. Pat. Nos. 4,683,195 and 4,683,202, which describe a method for increasing the concentration of a segment of a polynucleotide of interest in a mixture of genomic DNA without cloning or purification. This process for amplifying a polynucleotide of interest consists of introducing a large excess of two oligonucleotide primers to a DNA mixture containing the desired polynucleotide of interest, followed by a series of thermal cycles in the presence of a DNA polymerase. The two primers are complementary to each strand of the double-stranded polynucleotide of interest. The mixture is first denatured at a higher temperature, and then the primers are annealed to complementary sequences within the polynucleotide molecule of interest. After annealing, the primers are extended with a polymerase to form a new pair of complementary strands. The steps of denaturation, primer annealing, and polymerase extension can be repeated many times (called thermocycling) to obtain a high concentration of the amplified segment of the desired polynucleotide of interest. The length of the amplified segment (amplicon) of the desired polynucleotide of interest is determined by the relative positions of the primers with respect to each other, and therefore this length is a controllable parameter. By repeating this process, the method is called "polymerase chain reaction" (hereinafter "PCR"). Because the desired amplified segments of the polynucleotide of interest become the predominant nucleic acid sequences (in terms of concentration) in the mixture, they are said to be "PCR amplified". In a modification to the above method, the target nucleic acid molecule can be PCR amplified using multiple different primer pairs, optionally more than one primer pair per target nucleic acid molecule of interest, thereby forming a multiplex PCR reaction.

[0033] As used herein, the term "primer" and its derivatives generally refer to any polynucleotide that can hybridize to a target sequence of interest. Typically, a primer serves as a substrate onto which nucleotides can be polymerized by a polymerase, but in some embodiments, a primer can be incorporated into a synthesized nucleic acid strand to provide a site to which another primer can hybridize to prime a new strand that is complementary to the synthesized nucleic acid molecule. A primer can contain any combination of nucleotides or analogs thereof. In some embodiments, a primer is a single-stranded oligonucleotide or polynucleotide. The terms "polynucleotide" and "oligonucleotide" are used interchangeably herein to refer to a polymeric form of nucleotides of any length and can include ribonucleotides, deoxyribonucleotides, analogs thereof, or mixtures thereof. The term should be understood to include, as equivalents, any analog of DNA or RNA made from nucleotide analogs and is applicable to single-stranded (e.g., sense or antisense) and double-stranded polynucleotides. The term as used herein also encompasses cDNA, which is a complementary or copy DNA produced from an RNA template, for example, by the action of reverse transcriptase. This term refers only to the primary structure of the molecule. Thus, this term includes triple-, double-, and single-stranded deoxyribonucleic acid ("DNA"), as well as triple-, double-, and single-stranded ribonucleic acid ("RNA").

[0034] As used herein, the terms "library" and "sequencing library" refer to a collection or plurality of template molecules that share a common sequence at their 5' ends and a common sequence at their 3' ends. A collection of template molecules that contain known common sequences at their 3' and 5' ends is also referred to as a 3' and 5' modified library.

[0035] The term "flow cell" as used herein refers to a chamber that includes a solid surface across which one or more fluidic reagents can flow. Examples of flow cells and related fluidic systems and detection platforms that can be easily used in the method of the present disclosure are described in, for example, Bentley et al., Nature 456:53-59 (2008), WO 04 / 018497; U.S. Patent No. 7,057,026; WO 91 / 06678; WO 07 / 123744; U.S. Patent No. 7,329,492; U.S. Patent No. 7,211,414; U.S. Patent No. 7,315,019; U.S. Patent No. 7,405,281; and U.S. Patent Application Publication No. 2008 / 01082.

[0036] The term "amplicon" as used herein, when used in reference to a nucleic acid, refers to a product of copying a nucleic acid, which has a nucleotide sequence that is the same as or complementary to at least a portion of the nucleotide sequence of the nucleic acid. An amplicon can be produced by any of a variety of amplification methods using a nucleic acid or its amplicon as a template, including, for example, PCR, rolling circle amplification (RCA), ligation extension, or ligation chain reaction. An amplicon can be a nucleic acid molecule that has a single copy of a particular nucleotide sequence (e.g., a PCR product) or multiple copies of a nucleotide sequence (e.g., a concatemer product of RCA). A first amplicon of a target nucleic acid is typically a complementary copy. Subsequent amplicons are copies made from the target nucleic acid or from the first amplicon after the generation of the first amplicon. Subsequent amplicons can have a sequence that is substantially complementary to the target nucleic acid or substantially identical to the target nucleic acid.

[0037] As used herein, the term "array" refers to a collection of sites that can be distinguished from one another according to their relative positions. Different molecules at different sites of an array can be distinguished from one another according to the site's position in the array. An individual site of an array can contain one or more molecules of a particular type. For example, a site can contain a single target nucleic acid molecule with a particular sequence, or a site can contain several nucleic acid molecules with the same sequence (and / or its complementary sequence). The sites of an array can be different features located on the same substrate. Exemplary features include, but are not limited to, wells in a substrate, beads (or other particles) in or on a substrate, protrusions from a substrate, ridges on a substrate, or channels in a substrate. The sites of an array can be separate substrates, each with different molecules. The different molecules attached to the separate substrates can be identified according to the substrate's position on a surface with which it is associated, or according to the substrate's position in a liquid or gel. Exemplary arrays with separate substrates located on a surface include, but are not limited to, those with beads in wells.

[0038] The term "next generation sequencing (NGS)" as used herein refers to a sequencing method that allows massively parallel sequencing of clonally amplified molecules and single nucleic acid molecules. Non-limiting examples of NGS include sequencing-by-synthesis using reversible dye terminators and sequencing-by-ligation.

[0039] As used herein, the term "sensitivity" equals the number of true positives divided by the sum of true positives and false negatives.

[0040] As used herein, the term "specificity" is equal to the number of true negatives divided by the sum of true negatives and false positives.

[0041] The term "enrich" as used herein refers to a method of amplifying nucleic acids contained in a portion of a sample. Enrichment includes specific enrichment, which targets a particular sequence, such as a polymorphic sequence, and non-specific enrichment, which amplifies the entire genome of a DNA fragment of a sample.

[0042] As used herein, the term "each," when used in reference to a collection of items, is intended to identify each individual item in the collection, but does not necessarily refer to every item in the collection, unless the context clearly dictates otherwise.

[0043] As used herein, "providing" in the context of a composition, article, nucleic acid, or nucleus means making the composition, article, nucleic acid, or nucleus, purchasing the composition, article, nucleic acid, or nucleus, or otherwise obtaining the compound, composition, article, article, or nucleus.

[0044] The term "and / or" means one or all of the listed elements or a combination of any two or more of the listed elements.

[0045] The words "preferred" and "preferably" refer to embodiments of the present disclosure that may provide certain benefits, under certain circumstances. However, other embodiments may also be preferred, under the same or different circumstances. Furthermore, the recitation of one or more preferred embodiments does not imply that other embodiments are not useful, and is not intended to exclude other embodiments from the scope of the present disclosure.

[0046] The terms "comprises" and variations thereof do not have a limiting meaning where these terms appear in the description and claims.

[0047] Wherever embodiments are described herein with words such as "comprising" or "comprising", it is understood that otherwise similar embodiments are also provided which are described with the terms "consisting of" and / or "consisting essentially of".

[0048] Unless otherwise specified, "a," "an," "the," and "at least one" are used interchangeably and mean one or more.

[0049] Also herein, the recitations of numerical ranges by endpoints include all numbers subsumed within that range (eg, 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.).

[0050] Throughout this specification, references to "one embodiment," "an embodiment," "particular embodiment," "some embodiments," or the like, mean that the particular features, configurations, compositions, or characteristics described in connection with the embodiment are included in at least one embodiment of the disclosure. Thus, the appearances of such phrases in various places throughout this specification are not necessarily all referring to the same embodiment of the disclosure. Furthermore, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

[0051] For any method disclosed herein that includes distinct steps, the steps may be performed in any feasible order, and, if desired, any combination of two or more steps may be performed simultaneously.

[0052] The above summary of the present disclosure is not intended to describe each disclosed embodiment or every implementation of the present disclosure. The following description more particularly illustrates specific embodiments. In several places throughout the application, guidance is provided through lists of examples, which examples can be used in various combinations. In each instance, the recited list serves only as a representative group and should not be interpreted as an exclusive list. [Brief description of the drawings]

[0053] [Figure 1] Schematic diagram of the relationship between placental health, maternal response, and fetal response. [Diagram 2] Origin of circular RNA (C-RNA). [Diagram 3] Library construction workflow for C-RNA. [Figure 4] Validation of the C-RNA methodology comparing late pregnancy and non-pregnancy samples. [Diagram 5] Validation of the C-RNA method using longitudinal pregnancy samples. [Figure 6] Clinical trial description. [Figure 7] Sequencing data characteristics. [Figure 8] Classification of PEs without gene selection, relying on the entire dataset. [Figure 9] Explanation of the bootstrap method. [Figure 10] Classification of preeclampsia samples using bootstrap methods. [Figure 11] A search for excess preeclampsia genes. [Figure 12] Standard Adaboost model. [Figure 13] Independent cohorts will allow further validation of the preeclampsia signature. [Figure 14] Performance of standard adaboost models in classifying preeclampsia. [Figure 15] Classification of preeclampsia using standard DEX TREAT assay. [Figure 16] Gene selection and classification of preeclampsia using the jackknife method. [Figure 17] Validation of TREAT, bootstrap, and jackknife methods in an independent PEARL Biobank cohort. [Figure 18] Schematic representation of the bioinformatics approach to construct the AdaBoost Refined model. [Figure 19] Relative abundance of genes used by AdaBoost Refined models and their predictive ability in an independent dataset. [Figure 20]Identification of preeclampsia-specific C-RNA signatures in Nextera Flex-generated libraries using standard TREAT analysis and jackknife methods. [Figure 21] Relative abundance of genes used by AdaBoost Refined models in Nextera Flex-generated libraries and their predictive power in the RGH14 dataset. [Figure 22] Validation of a clinic-friendly whole-exome C-RNA analysis method. Figure 22A is a schematic diagram of the sequencing library generation method; all steps after blood collection can be performed in a centralized processing laboratory. The time course of transcripts was modified throughout pregnancy (Figure 22B). Overlap of genes identified in the C-RNA pregnancy progression study (Figure 22C). Tissue expressing 91 genes unique to the pregnancy progression study (Figure 22D). [Diagram 23] Sample collection for PE clinical trials. Panels show time of blood collection (triangles) and gestational age at birth (squares) for each individual in the iPC (Figure 23A) and PEARL (Figure 23B) studies. Red lines indicate the threshold for full-term birth. Preterm birth rates are significantly higher in the early-onset PE cohort (Figure 23C). ***p<0.001 by Fisher's exact test. [Figure 24] Differential analysis of C-RNA identifies preeclampsia biomarkers. Transcript fold change and abundance were altered in PE (Figure 24A). One-sided confidence p-value intervals were calculated after jackknife analysis for each gene detected by standard analytical methods (Figure 24B). (21) Transcript abundance fold change determined by whole exome sequencing and qPCR for genes (Figure 24C). *p<0.05 by Student's T-test. Tissue distribution of disease genes (Figure 24D). Hierarchical clustering of iPC samples (average linkage, squared Euclidean distance) (Figure 24E). Clustering of early-onset PE (Figure 24F) and late-onset PE (Figure 24G) samples from the PEARL study. [Diagram 25]AdaBoost classifies preeclampsia samples across cohorts. Heatmap showing relative abundance of transcripts used by machine learning in each cohort (Figure 25A). The height of each block reflects the importance of each gene. ROC curves for each dataset (Figure 25B). Distribution of AdaBoost scores (KDE). Orange lines indicate optimal boundaries for distinguishing PE and control samples (Figure 25C). Concordance between genes identified by differential analysis and those used in AdaBoost (Figure 25D). Tissue distribution of AdaBoost genes (Figure 25E). [Figure 26] C-RNA data integrity when blood is stored in different blood collection tubes. Abundance of pre-detected C-RNA pregnancy markers from blood stored overnight in different tube types is compared with immediate processing after collection into EDTA tubes (Figure 26A). Scatter plot comparing transcript FPKM values ​​for C-RNA prepared from the same individual after different blood storage periods (Figure 26B). Pearson's correlation coefficient R is more variable with EDTA tubes (cf, cell-free) (Figure 26C). [Figure 27] Effect of plasma volume on C-RNA data quality. To determine the appropriate plasma input for the protocol, a meta-analysis was performed using data from nine independent studies. Noise (biological coefficient of variation, EdgeR) was calculated from biological replicates within each study (Figure 27A). Library complexity (edge ​​population, Preseq) was calculated for each sample (Figure 27B). **p<0.01, ***p<0.001 by ANOVA with Tukey's HSD correction using study as the blocking variable. [Figure 28] Pregnancy marker tissue specificity. Pie charts showing the tissue specificity of genes detected during pregnancy by three independent studies using either the modified gene set (Figure 28A), transcripts specific to each study (Figure 28B), or the crossover gene set (Figure 28C). [Figure 29]Jackknife filtering excludes genes that are not widely altered in preeclampsia. Schematic of the jackknife method used to determine how consistently transcripts were altered across PE samples (Figure 29A). Mean abundance and noise for each differentially abundant gene (Figure 29B). ROC area under the curve values ​​for each affected transcript provide an indication of how separate C-RNA transcript abundance distributions exist for control and PE samples (Figure 29C). *p<0.05 by Mann-Whitney U test. Hierarchical clustering of iPC samples with genes excluded after jackknife filtering (Figure 29D). Tissue distribution of excluded transcripts (Figure 29E). The reduced fetal and placental contributions may suggest that the maternal component of PE is the most variable between individuals. [Diagram 30]AdaBoost model development strategy. The RGH014 dataset was split into six pieces (Figure 30A). The "holdout subset" contained 10% of samples (randomly selected) as well as three samples that were incorrectly clustered when using differentially abundant genes (as in Figure 24C) and was completely excluded from model building. The remaining samples were randomly split into five uniformly sized "test subsets". For each test subset, the training data consisted of all non-holdout and non-test samples. Gene counts for training and test data were TMM normalized in edgeR and then standardized to a mean of 0 and standard deviation of 1 for each gene. For each training / test sample set, AdaBoost models (90 estimators, 1.6 learning rate) were built from the training data 10 times (Figure 30B). Feature pruning was performed to remove genes following gradually increasing importance thresholds, and performance was evaluated by the Matthews correlation coefficient when predicting the test data. The model with the best performance (and in case of a tie, the fewest genes) was retained. Estimators from all 50 independent models were combined into a single AdaBoost model (Figure 30C). Feature pruning was performed on the resulting ensemble, this time using the percentage of models that used genes to set a threshold and performance, measured by the average log loss value over the test subset. ROC curve after applying the final AdaBoost model to the holdout data (Figure 30D). All samples were accurately separated, except for two of the three samples that were similarly misclustered by HCA. [Diagram 31]Impact of hyperparameter selection and feature pruning on machine learning performance. Heatmap of grid search to identify optimal hyperparameters for AdaBoost (Fig. 31A). Matthews correlation coefficient was used as a performance metric. Flattened plot for each hyperparameter (Fig. 31B). Arrows indicate values ​​selected for model building. Fig. 31C shows the impact of pruning individual AdaBoost models on performance (as in Fig. 30B). Solid lines are the average of all 10 models, and shaded areas indicate standard deviation. Number of AdaBoost models using each gene observed in the pre-pruned ensemble (Fig. 31D). Model performance when pruning the combined AdaBoost ensemble (Fig. 31E). Orange lines in Fig. 31D and Fig. 31E indicate the threshold applied to generate the final AdaBoost model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0054] Schematic diagrams are not necessarily to scale. Like numbers used in the drawings may refer to like components. However, it will be understood that the use of a number to refer to a component in a given figure is not intended to limit the component in another figure that is labeled with the same number. Furthermore, the use of different numbers to refer to components is not intended to indicate that the differently numbered components may not be identical or similar to the other numbered components.

[0055] Provided herein are signatures of circulating RNA found in the maternal circulation that are specific for preeclampsia, and the use of such signatures in non-invasive methods for diagnosing preeclampsia and identifying pregnant women at risk for developing preeclampsia.

[0056] The majority of DNA and RNA in the body is located intracellularly, but extracellular nucleic acids are also found circulating freely in the blood. Circulating RNA, also referred to herein as "C-RNA," refers to the extracellular segments of RNA found in the bloodstream. C-RNA molecules are derived primarily from two sources: first, released into the circulation from dead cells undergoing apoptosis, and second, contained within exosomes that are excreted by viable cells into the circulation. Exosomes are small membranous vesicles approximately 30-150 nm in diameter that are released into the extracellular space from many cell types and found in various body fluids (including serum, urine, and breast milk) and carry proteins, mRNAs, and microRNAs. The lipid bilayer structure of exosomes protects the RNA contained therein from degradation by RNases and provides stability in the blood. See, e.g., Huang et al., 2013, BMC Genomics; 14:319; and Li et al., 2017, Mol Cancer; 16:145). There is growing evidence that exosomes have specialized functions and play a role in processes such as coagulation, cell-cell signaling, and waste management (van der Pol et al., 2012, Pharmacol Rev; 64(3): 676-705). See also Samos et al., 2006, Ann NY Acad Sci; 1075: 165-173; Zernecke et al., 2009, Sci Signal; 2: ra81; Ma et al., 2012, J Exp Clin Cancer Res; 31: 38; and Sato-Kuwabara et al., 2015, Int J Oncol; 46: 17-27.

[0057] In the methods described herein, C-RNA molecules found in the maternal circulation serve as biomarkers of fetal, placental and maternal health, providing a means of pregnancy progression. Described herein are C-RNA signatures in the maternal circulation that indicate pregnancy, C-RNA signatures in the maternal circulation that are temporally associated with gestational age, and C-RNA signatures in the maternal circulation that indicate the pregnancy complication preeclampsia.

[0058] C-RNA signatures in the maternal circulation indicative of preeclampsia include ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, P The C-RNA signature comprises a plurality of C-RNA molecules encoding at least a portion of a plurality of proteins selected from PP1R17, TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3. The C-RNA signature is the Adaboost General signature obtained using the TruSeq library generation method shown in Table 1 below, also referred to herein as "List (a)" or "(a)".

[0059] The C-RNA signature in the maternal circulation indicative of preeclampsia comprises a plurality of C-RNA molecules encoding at least a portion of a plurality of proteins selected from TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4. This C-RNA signature is a bootstrap signature obtained using the TruSeq library generation method shown in Table 1 below, also referred to herein as "List (b)" or "(b)".

[0060] C-RNA signatures in the maternal circulation indicative of preeclampsia include CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, E MP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, ALPK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L, CA3, HR G, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37, ZEB1, JAG2 , KIF26A, APOLD1, PNMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B , SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMP5, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5. This C-RNA signature is a standard DEX Treat signature obtained using the TruSeq library generation method shown in Table 1 below, also referred to herein as "List (c)" or "(c)".

[0061] A C-RNA signature in the maternal circulation indicative of preeclampsia comprises a plurality of C-RNA molecules encoding at least a portion of a protein selected from VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4. This C-RNA signature is a jackknife signature obtained using the TruSeq library generation method shown in Table 1 below, also referred to herein as "list (d)" or "(d)".

[0062] The C-RNA signature in the maternal circulation indicative of preeclampsia comprises a plurality of C-RNA molecules encoding at least a portion of a protein selected from ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4. This C-RNA signature is a standard DEX Treat signature obtained using the Nextera Flex for Enrichment library generation method shown in Table 1 below, also referred to herein as "List (e)" or "(e)".

[0063] The C-RNA signature in the maternal circulation indicative of preeclampsia comprises a plurality of C-RNA molecules encoding at least a portion of a protein selected from ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4. This C-RNA signature is a jackknife signature obtained using the Nextera Flex Enrichment library generation method shown in Table 1 below, also referred to herein as "List (f)" or "(f)".

[0064] A C-RNA signature in the maternal circulation indicative of preeclampsia comprises a plurality of C-RNA molecules encoding at least a portion of a protein selected from CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES. This C-RNA signature is an Adaboost Refined TruSeq signature obtained using the TruSeq library generation method shown in Table 1 below, also referred to herein as "AdaBoost Refined 1", "List (g)" or "(g)".

[0065] In some embodiments, C-RNA signatures in the maternal circulation indicative of preeclampsia include ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4 (also referred to herein as "AdaBoost Refined 2"), ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4 (also referred to herein as "AdaBoost Refined 3"), ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4 (also referred to herein as "AdaBoost Refined 4"), ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4 (also referred to herein as "AdaBoost Refined 5"), ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4 (also referred to herein as "AdaBoost Refined 6"). AdaBoost Refined 5), ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL (also referred to herein as "AdaBoost Refined 6"), or ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL (also referred to herein as "AdaBoost Refined 7").

[0066] The C-RNA signature in the maternal circulation indicative of preeclampsia comprises a plurality of C-RNA molecules encoding at least a portion of a protein selected from LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1. This C-RNA signature is an Adaboost Refined Nextera Flex signature obtained using the Nextera Flex for Enrichment library generation method set forth in Table 1 below, also referred to herein as "List (h)" or "(h)".

[0067] In some embodiments, a C-RNA signature in the maternal circulatory system indicative of preeclampsia comprises a plurality of C-RNA molecules encoding at least a portion of a protein selected from any one or more of any of (a), (b), (c), (d), (e), (f), (g), and / or (h) in combination with any one or more of any of (a), (b), (c), (d), (e), (f), (g), and / or (h).

[0068] The examples provided herein describe the eight gene lists summarized above that distinguish preeclampsia and control pregnancies. Each was identified by using different analytical methods and / or different data sets. However, there is a high degree of concordance between many of these gene sets. Identifying a transcript as altered in preeclampsia C-RNA using multiple methods indicates that the transcript has a higher predictive value for classification of the disease. Therefore, the importance of the transcripts identified by all differential expression analyses and all AdaBoost models was combined and ranked. Genes assigned lower ranks are not important or informative, and they may be less robust for classification of preeclampsia across cohorts and sample preparations.

[0069] First, transcripts identified when using all differential expression analyses (standard DEX Treat, bootstrap and jackknife) for both library construction methods (TruSeq and Nextera Flex for Enrichment) were combined. Table 2 below shows the relative importance for all 125 transcripts identified by the different analysis methods. Transcripts identified across all analysis methods and both library constructions are the strongest classifiers and are assigned an importance ranking of 1. Transcripts identified by three or more analysis methods and detected in both library constructions were given an importance ranking of 2. Transcripts identified by the jackknife method, the most stringent analysis method, as well as only one of the library constructions, were assigned an importance ranking of 3. Transcripts identified by two of the five analysis methods were given an importance ranking of 4. Transcripts identified only by the standard DEX Treat method, the most broad and comprehensive analysis, were given the lowest importance ranking of 5.

[0070] We then combined the 91 transcripts identified across all AdaBoost models (AdaBoost General and AdaBoost Refined) and both library generations (Table 3 below). When generating refined AdaBoost models for each library generation, slight variations were observed in the resulting gene sets each time a model was built from the same data. This is a natural consequence of the randomness used by AdaBoost to search through the large whole-exome C-RNA data. To obtain a representative list of genes, model building for refined AdaBoost was performed a minimum of nine separate times, and all genes used by one or more models were reported. The percentage of models that included each transcript is reported in Table 3 (frequency of use by AdaBoost). AdaBoost assigns its own "importance" value to each transcript, which reflects how much the abundance of that transcript influences the decision of whether a sample is from a preeclampsia patient. These AdaBoost importance values ​​were averaged across each refined AdaBoost model that used a given transcript (Table 3, average AdaBoost model importance).

[0071] Transcripts identified across all AdaBoost analyses and library constructions were assigned the highest importance ranking of 1. Transcripts identified in the refined AdaBoost models for a single library construction method with a frequency of more than 90% used by AdaBoost were assigned an importance ranking of 2. In general, these transcripts have higher AdaBoost model importance, consistent with improved predictive ability. Transcripts identified in the refined AdaBoost models for a single library construction method, but used by less than 90% of the AdaBoost models, were assigned an importance ranking of 3. Transcripts identified only in the general AdaBoost model for a single library construction were given the lowest importance ranking of 4.

[0072] Table 2 lists all genes identified by DEX analysis across all analytical methods and library constructions. Rank 1 = transcripts identified across all analytical and library construction methods. Rank 2 = transcripts identified in both library constructions and 3 / 5 analytical methods. Rank 3 = identified in one library construction method, the Jackknife method, which is the most stringent analysis. Rank 4 = identified in 2 / 5 analyses. And Rank 5 = identified only in the standard DEX Treat method, which is the least stringent analysis method.

[0073] Table 3 lists all genes identified by Adaboost analysis across both library productions: Rank 1 = identified in both library production methods and the refined adaboost model; Rank 2 = identified in one library production method that was present in the refined adaboost model with high model importance and frequency; Rank 3 = identified in one library production method that was present in the refined adaboost model with moderate model importance and frequency; and Rank 4 = identified in one library production that was not present in the refined adaboost model.

[0074] Table 4 below is a glossary of all of the terms for the various genes listed herein. This information was obtained from the HUGO Gene Nomenclature Committee of the European Bioinformatics Institute.

[0075] [Table 1]

[0076] [Table 2]

[0077]

Table 3

[0078]

Table 4

[0079]

Table 5

[0080]

Table 6

[0081]

Table 7

[0082]

Table 8

[0083]

Table 9

[0084]

Table 10

[0085]

Table 11

[0086]

Table 12

[0087] The term "multiple" refers to two or more elements. For example, the term is used herein in reference to several C-RNA molecules that act as a signature indicative of preeclampsia.

[0088] A plurality may be any two, any three, any four, any five, any six, any seven, any eight, any nine, any ten, any eleven, any twelve, any thirteen, any fourteen, any fifteen, any sixteen, any seventeen, any eighteen, any nineteen, any twenty, any twenty-one, any twenty-two, any twenty-three, any twenty-four, any twenty-five, any twenty-six, any twenty-seven, any twenty-eight, any twenty-nine ... 30 of any, 31 of any, 32 of any, 33 of any, 34 of any, 35 of any, 36 of any, 37 of any, 38 of any, 39 of any, 40 of any, 41 of any, 42 of any, 43 of any, 44 of any, 45 of any, 46 of any, 47 of any, 48 of any, 49 of any, 50 of any, 51 of any, 52 of any, 53 of any, 54 of any, 55 of any, 56 of any, 57 of any, 58 of any, 59 of any, 60 of any, 61, any 62, any 63, any 64, any 65, any 66, any 67, any 68, any 69, any 70, any 71, any 72, any 73, any 74, any 75, any 76, any 77, any 78, any 79, any 80, any 81, any 82, any 83, any 84, any 85, any 86, any 87, any 88, any 89, any 90, any 91, any 92, any 93, any 94, any 95, any 96, any 97, any 98, any 99, any 100, any 101, any 102, any 103, any 104, any 105, any 106, any 107, any 108, any 109, any 110, any 111, any 112, any 113, any 114, any 115, any 116, any 117, any 118, any 119, any 120,The plurality may include any 121, any 122, or any 123. The plurality may include at least any of the values ​​listed above. The plurality may include more than any of the values ​​listed above. The plurality may include any range of those listed above. In some embodiments, a C-RNA signature indicative of preeclampsia includes only one of the biomarkers listed above.

[0089] Identification and / or quantification of one of these C-RNA signatures in a sample taken from a subject can be used to determine whether the subject is suffering from preeclampsia or is at risk of developing preeclampsia.

[0090] The sample may be a biological sample, including, but not limited to, blood, serum, plasma, sweat, tears, urine, sputum, lymph, saliva, amniotic fluid, tissue biopsy, swab, or smear (including, but not limited to, a placental tissue sample). In some preferred embodiments, the biological sample is a cell-free plasma sample. The biological sample may be a maternal sample taken from a pregnant subject.

[0091] As used herein, the term "subject" refers to human subjects as well as non-human mammalian subjects. Although the examples herein relate to humans and the term is primarily directed to human concerns, the concepts of the present disclosure are applicable to any mammal and are useful in veterinary medicine, animal science fields, research laboratories, and the like.

[0092] The subject may be a pregnant woman, including a pregnant woman at any gestational age. The gestational age may be, for example, early pregnancy, mid pregnancy (including late mid pregnancy), or late pregnancy (including early late pregnancy). The gestational age may be, for example, before 16 weeks, before 20 weeks, or after 20 weeks. The gestational age may be, for example, 8-18 weeks, 10-14 weeks, 11-14 weeks, 11-13 weeks, or 12-13 weeks.

[0093] The discovery of cell-free fetal nucleic acids in maternal plasma has opened up new possibilities for non-invasive prenatal diagnosis. Over the last few years, several approaches have been demonstrated to enable such circulating fetal nucleic acids to be used for the prenatal detection of chromosomal aneuploidies. For example, Poon et al.,2000,Clin Chem;1832-4;Poon et al.,2001,Ann NY Acad Sci;945:207-10;Ng et al.,2003,Clin Chem;49(5):727-31;Ng et al.,2003,Proc Natl Acad Sci US A.;100(8):4748-53;Tsui et al.,2004,J Med Genet;41(6):461-7;Go et al.,2004,Clin Chem;50(8):1413-4;Smets et al.,2006,Clin Chim Acta;364(1-2):22-32;Tsui et al. al.,2006,Methods Mol Biol;336:123-34;Purwosunu et al.,2007,Clin Chem;53(3):399-404;Chim et al.,2008,Clin Chem;54(3):482-90;Tsui and Lo,2008,Methods Mol Biol;444:275-89;Lo,2008,Ann NY Acad Sci;1137:140-143;Miura et al. al.,2010,Prenat Diagn;30(9):849-61;Li et al.,2012,Clin Chim Acta;413(5-6):568-76;Williams et al.,2013,Proc Natl Acad Sci USA;110(11):4255-60;Tsui et al.,2014,Clin Chem;60(7):954-62;Tsang Any of the methods described in U.S. Pat. Appl. Pub. No. 2014 / 0243212 may be used in the methods described herein.

[0094] Detection and identification of biomarkers of C-RNA signatures in the maternal circulation that are indicative of preeclampsia or the risk of developing preeclampsia may involve any of a variety of techniques. For example, biomarkers may be detected in serum by radioimmunoassay methods or polymerase chain reaction (PCR) techniques may be used.

[0095] In various embodiments, identifying biomarkers of C-RNA signatures in the maternal circulation that are indicative of preeclampsia or the risk of developing preeclampsia may include sequencing the C-RNA molecules. Any of a number of sequencing techniques may be used, including, but not limited to, any of a variety of high-throughput sequencing techniques.

[0096] In some embodiments, the C-RNA population in the maternal biological sample may be subjected to enrichment of RNA sequences, including protein-coding sequences, prior to sequencing, using a platform including, but not limited to, the Agilent SureSelect Human All Exon platform (Chen et al., 2015a, Cold Spring Harb Protoc; 2015(7):626-33. doi:10.1101 / pdb.prot083659); the Roche NimbleGen SeqCap EZ Exome Library SR platform (Chen et al., 2015b, Cold Spring Harb Protoc; 2015(7):634-41. doi:10.1101 / pdb.prot084855); or the Illumina TruSeq Exome Enrichment platform (Chen et al., 2015c, Cold Spring Harb Protoc; 2015(7):634-41. doi:10.1101 / pdb.prot084855). Any of a variety of platforms available for whole exome enrichment and sequencing can be used, including those described in the "TruSeq™ Exome Enrichment Guide", Catalog # FC-930-1012 Part # 15013230 Rev.B November 2010, and Illumina's "TruSeq™ RNA Sample Preparation Guide", Catalog #RS-122-9001DOC Part # 15026495 Rev.F March 2014.

[0097] In certain embodiments, biomarkers of C-RNA signatures in the maternal circulatory system that indicate preeclampsia or the risk of developing preeclampsia can be detected and identified using microarray technology. In this method, polynucleotide sequences of interest are arranged or arrayed on a microchip substrate. The arrayed sequences are then hybridized with maternal biological samples, or purified and / or enriched portions thereof. Microarrays can include a variety of solid supports, including but not limited to beads, microscope slides, glass wafers, gold, silicon, microchips, and other plastic, metal, ceramic, or biological surfaces. Microarray analysis can be performed by commercially available equipment, such as by using Illumina technology, following manufacturer's protocols.

[0098] With regard to the collection, transport, storage, and / or processing of blood samples for the preparation of circulating RNA, measures may be taken to stabilize the sample and / or prevent the disruption of cell membranes that would result in the release of cellular RNA into the sample. For example, in some embodiments, blood samples may be collected, transported, and / or stored in tubes with cell and DNA stabilizing properties, such as Streck Cell-Free DNA BCT® blood collection tubes, before being processed into plasma. In some embodiments, blood samples are not exposed to EDTA. See, for example, Qin et al., 2013, BMC Research Notes; 6:380 and Medina Diaz et al., 2016, PLoS ONE; 11(11): e0166354.

[0099] In some embodiments, the blood sample is processed into plasma within about 24 to about 72 hours of blood collection, and in some embodiments, within about 24 hours of blood collection, hi some embodiments, the blood sample is kept, stored, and / or transported at room temperature prior to being processed into plasma.

[0100] In some embodiments, blood samples are maintained, stored, and / or transported without exposure to low temperatures (e.g., on ice) or without freezing prior to being processed into plasma.

[0101] The present disclosure includes a kit for use in diagnosing preeclampsia and identifying pregnant women at risk of developing preeclampsia.A kit is any product (e.g., package or container) that includes at least one reagent, e.g., probe, for specifically detecting the C-RNA signature in the maternal circulatory system described herein, which indicates preeclampsia or the risk of developing preeclampsia.A kit can be promoted, distributed, or sold as a unit for carrying out the method of the present disclosure.

[0102] The use of the signature of circulating RNA found in the maternal circulation specific for preeclampsia in a non-invasive method for diagnosing preeclampsia and identifying pregnant women at risk of developing preeclampsia can be combined with appropriate monitoring and medical management. For example, further tests can be ordered. Such tests include, for example, blood tests to measure liver function, kidney function, and / or platelets and various coagulation proteins, urine analysis to measure protein or creatinine levels, fetal ultrasound to monitor and measure fetal growth, weight, and amniotic fluid, non-stress tests to measure how fast the fetal heart rate is with fetal movement, and / or biophysical profiles using ultrasound to measure fetal breathing, muscle tone, and movement and amniotic fluid volume can be ordered. Therapeutic interventions can include, for example, increased frequency of prenatal visits, antihypertensive drugs to reduce blood pressure, corticosteroid drugs, anticonvulsants, bed rest, hospitalization, and / or early delivery. See, e.g., Townsend et al., 2016 “Current best practice in the management of hypertensive disorders in pregnancy”, Integr Blood Press Control; 9:79-94.

[0103] Therapeutic interventions may include administration of low-dose aspirin to pregnant women identified as at risk for developing preeclampsia. A recent multicenter double-blind controlled trial demonstrated that treatment of women at high risk for early preeclampsia with low-dose aspirin reduced the incidence of this diagnosis compared to placebo (Rolnik et al., 2017, “Aspirin versus Placebo in Pregnancies at High Risk for Preterm Preeclampsia”, N Engl J Med; 377(7):613-622). Doses of low-dose aspirin include, but are not limited to, about 50 to about 150 mg per day, about 60 to about 80 mg per day, about 100 mg or more per day, or about 150 mg per day. Administration may begin, for example, at or before 16 weeks of gestation or 11-14 weeks of gestation. Administration may continue until 36 weeks of gestation.

[0104] The present invention is illustrated by the following examples, it being understood that the particular examples, materials, amounts and procedures are to be interpreted broadly in accordance with the scope and spirit of the invention described herein. EXAMPLES

[0105] Example 1 Pregnancy-specific C-RNA signature The presence of circulating nucleic acids in maternal plasma provides a means of knowing the progress and health of the fetus and placenta (Figure 1). Circulating RNA (C-RNA) is detected in the maternal circulation and originates from two main sources. A significant proportion of C-RNA originates from apoptotic cells that release vesicles containing C-RNA into the bloodstream. C-RNA also enters the maternal circulation by the release of active signaling vesicles such as exosomes and microvesicles from various cell types. As shown in Figure 2, C-RNA is therefore composed of by-products of cell death as well as active signaling products. Properties of C-RNA include production by a common process, release from cells throughout the body, and being stable and contained in vesicles. It represents the circulating transcriptome that reflects tissue-specific changes in gene expression, signaling, and cell death.

[0106] C-RNA has the potential to be a good biomarker for at least the following reasons: 1) All C-RNA is fairly stable in blood because it is contained within membrane-bound vesicles that protect the C-RNA from degradation. 2) C-RNA originates from all cell types. For example, C-RNA has been shown to contain transcripts from both the placenta and the developing fetus. The diverse origins of C-RNA make it a potential treasure trove for obtaining information about both fetal and overall maternal health.

[0107] C-RNA libraries were generated from plasma samples using standard Illumina library generation and whole exome enrichment techniques. This is shown in Figure 3. Specifically, Illumina TruSeq™ library generation and RNA Access Enrichment were used. Using this approach, libraries with 90% of reads aligning to human coding regions were generated (Figures 3 and 7). Samples were downsampled to 50M reads and 40M or more mapped reads were used for downstream analysis. Samples were processed using the C-RNA workflow shown in Figure 3. Dual indexed libraries. Sequenced at 50x50 on a Hiseq2000.

[0108] As shown in Figure 4, the results of plasma samples from pregnant women in late pregnancy are compared with plasma samples from non-pregnant women, revealing a clear pregnancy-specific signature.The top 20 differential abundance genes of this signature are CSHL1, CSH2, KISS1, CGA, PLAC4, PSG1, GH2, PSG3, PSG4, PSG7, PSG11, CSH1, PSG2, HSD3B1, GRHL2, LGALS14, FCGR1C, PSG5, LGALS13, and GCM1.The majority of genes identified in the pregnancy signature are expressed in the placenta, and are also correlated with published data.These results also confirm that placental RNA can be obtained in maternal circulation.

[0109] Example 2 C-RNA signature across gestational age This example characterizes the C-RNA signatures across different gestational ages throughout pregnancy.It is expected that the change in C-RNA signatures across different longitudinal time points throughout pregnancy is less than the difference between the C-RNA signatures of pregnant and non-pregnant samples shown in Example 1.As shown in Figure 5, a clear time course of the C-RNA profile of signature genes is observed as pregnancy progresses, with a clear group of genes that are upregulated in early pregnancy and a clear group of genes that increase in late pregnancy.

[0110] These genes include CGB8, CGB5, ZSCAN23, HSPA1A, PMAIP1, C8orf4, ITM2B, IFIT2, CD74, HSPA6, TFAP2A, TRPV6, EXPH5, ​​CAPN6, ALDH3B2, RAB3B, MUC15, GSTA3, GRHL2, and CSHL1, as listed in Figure 5.

[0111] These genes may also include CSHL1, CSH2, KISS1, CGA, PLAC4, PSG1, GH2, PSG3, PSG4, PSG7, PSG11, CSH1, PSG2, HSD3B1, GRHL2, LGALS14, FCGR1C, PSG5, LGALS13, and GCM1.

[0112] These changes throughout pregnancy correlate with published data from both Steve Quake and Dennis Lo. See, e.g., Maron et al., 2007, “Gene expression analysis in pregnant women and their infants identify unique fetal biomarkers that circulate in maternal blood”, J Clin Invest; 117(10): 3007-3019; Koh et al., 2014, “Noninvasive in vivo monitoring of tissue-specific global gene expression in humans”, Proc Natl Acad Sci USA; 111(20): 7361-6; and Ngo et al., 2018, “Noninvasive blood tests for fetal development predict gestational age and preterm delivery”, Science; 360(6393): 1133-1136. A C-RNA signature was found that correlated with patterns of placental gene expression. This technique can therefore detect subtle changes during pregnancy and provides a non-invasive means to monitor placental health.

[0113] Example 3 C-RNA signature of preeclampsia In this example, a C-RNA signature specific to preeclampsia was identified. The C-RNA signature was determined and assayed in samples taken from pregnant women diagnosed with preeclampsia from two studies, the RGH14 study (registered at clinical trials.gov as NCT0208494) and the Pearl study (also referred to herein as Pearl Biobank; registered at clinical trials.gov as NCT02379832) (Figure 6). Two tubes of blood were taken at the time of preeclampsia diagnosis. Eighty control samples were taken that were matched for gestational age to minimize transcriptional variability unrelated to preeclampsia pathology and to control for gestational age differences in C-RNA signature. Samples from the RGH14 study were used to identify a set of biologically relevant genes, and the predictive value of these biomarkers was validated in an independent cohort of samples from Pearl Biobank.

[0114] In analyzing the RGH14 data, C-RNA signatures specific to preeclampsia (PE) were identified using four different methods: TREAT, bootstrap, jackknife, and Adaboost. Example 3 focuses on the first three analytical methods, and Example 4 focuses on the Adaboost method.

[0115] The t-test relative to threshold (TREAT) statistical method using the EDGR program allows researchers to formally test (with associated p-values) whether expression differences in microarray experiments are greater than a given (biologically significant) threshold. For a more detailed description of the TREAT statistical method, see McCarthy and Smyth, 2009, "Testing significance relative to a fold-change threshold is a TREAT", Bioinformatics; 25(6): 765-71, and for a more detailed description of the EDGR program, see Robinson et al., 2010, "edgeR: a Bioconductor package for differential expression analysis of digital gene expression data", Bioinformatics; 26: 139-140. For a more detailed description of the Adaboost method, see Freund and Schapire, 1997, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting", Journal of Computer and Systems Sciences; 55(1): 119-139 and Pedregosa et al., 2011, "Scikit-learn: Machine Learning in Python", JMLR; 12: 2825-2830. The Adaboost method is described in Example 4.

[0116] In the first method, standard statistical tests (TREAT method) were used to identify genes that were statistically different in the RGH14 preeclampsia cohort of 40 patients compared to the subset of matched controls (40 patients). 122 genes were identified as statistically different in the preeclampsia cohort (40 patients) compared to the subset of matched controls (40 patients) (Figure 8, right panel). These genes were CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, EMP1, FLN C, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, ALPK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L , CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14o rf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, S EMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, L Including EP, NES, VSIG4, HBG2, CADM2, LAMP5, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5.

[0117] The TREAT method did not identify a set of genes that 100% accurately classifies preeclampsia patients into separate groups (Figure 15). However, focusing on these identified genes improved classification compared to using the entire dataset of all measured genes (Figure 8, left panel). This highlights the value of focusing on a subset of genes for prediction. However, in the TREAT method, a significant amount of variation was observed in the identified genes depending on the choice of control. To address this biological variability and further improve the predictive value of our gene list, a second bootstrap method was developed.

[0118] In the RGH14 study, more control samples (80) are available than preeclampsia patient samples (40). Therefore, the RGH14 cohort of 40 preeclampsia patient samples was compared to 40 randomly selected control samples (also matched for gestational age) to identify a list of genes that were statistically different in the preeclampsia cohort. As shown in Figure 9, this was then repeated 1,000 times to identify the frequency with which a set of genes were identified. A significant subset of genes appeared in fewer than 10 of the 1,000 repeats (less than 1% of the 1,000 repeats). These low frequency genes are most likely due to biological noise and may not reflect genes that are broadly preeclampsia specific. Therefore, we further narrowed the gene list selection by requiring that a gene be considered statistically different in the preeclampsia cohort only if it was identified in 50% of the 1,000 repeats performed (Figure 9, right panel). As shown in Figure 10, differential transcript abundance with additional bootstrap selection distinguishes preeclampsia samples from healthy controls. Using this additional requirement helped address biological variability and further improved the ability to accurately classify preeclampsia samples.

[0119] Using this bootstrap method, 27 genes were identified as statistically associated with preeclampsia. These genes include TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4. The genes identified by this bootstrap method had good agreement with published data. Approximately 75% of these genes are expressed by the placenta. As shown in Figure 11, there is overlap with known markers of preeclampsia, including PAPPA and CRH. And a significant number of these genes are involved in embryonic development, extracellular matrix remodeling, immune regulation, and cardiovascular function, all pathways known to be dysregulated in preeclampsia.

[0120] A third jackknife method was also developed to capture the subset of genes with the highest predictive value. This approach is similar to the bootstrap method. Patients from both preeclampsia and control groups were randomly subsampled to identify differentially abundant genes 1,000 times. Instead of using the frequency at which genes were identified as statistically different, the jackknife method calculated a confidence interval (95%, one-sided) for the p-value of each transcript. Genes with this confidence interval exceeding 0.05 were excluded (Figure 16, left panel).

[0121] Using the jackknife method, 30 genes were identified as predictive of preeclampsia: VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4.

[0122] As shown in the right panel of Figure 16, this approach gave good classification of preeclampsia patients in the RGH14 dataset (compare Figure 15 (TREAT), Figure 10 (bootstrap) and Figure 16 (jackknife)). Each identified gene list was also used to classify preeclampsia samples in an independent Pearl Biobank dataset. As shown in Figure 17, each gene list was able to classify preeclampsia samples.

[0123] All genes identified by bootstrap and jackknife methods are shown in the 122 TREAT method genes (Table 2, DEX analysis, TruSeq library generation method). The bootstrap and jackknife gene lists are highly concordant, with over 70% of genes in common. Approximately 90% of the transcripts identified by either method showed increased transcript abundance in preeclampsia patients, consistent with increased signaling and / or cell death in this disease.

[0124] Example 4 Identification of C-RNA signatures using Adaboost In this example, an alternative approach, a published machine learning algorithm called adaboost, was used to identify specific C-RNA signatures associated with preeclampsia. As shown in FIG. 12, this approach identifies a set of genes with the highest predictive ability to classify samples as preeclampsia (PE) or normal. Using this gene list, the clearest separation of the preeclampsia cohort from healthy controls was observed. However, this approach can also be highly prone to overfitting on the samples used to build the model. Therefore, a completely independent dataset from the PEARL study was used to validate the predictive model (FIG. 13). Using this Adaboost gene list, 85% of preeclampsia samples were correctly classified with 85% specificity (FIG. 14). Overall, the Adaboost machine learning approach builds the most accurate predictive model for preeclampsia.

[0125] Using the AdaBoost method, 75 genes were identified as statistically associated with preeclampsia (Table 3, AdaBoost analysis, TruSeq library construction method). These genes include ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, T These include NFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3.

[0126] A refined AdaBoost model was also developed for robust classification of PE samples. To generate a generalized machine learning model that can accurately predict new samples, we used a rigorous approach that avoided overfitting with a single dataset, and validated the final classifier with samples that were not used for model construction. As shown in Figure 18, the RGH14 dataset was divided into six pieces by random selection: a holdout subset that included 12% of samples that were excluded from model construction, and five uniformly sized test subsets. For each iteration, a subset was designated as training data or test sample. This process, starting with building an AdaBoost model, was repeated a minimum of 10 times for this data subset. After building 50 high-performing models for the five test-training subsets, the estimators from all models were combined into a single AdaBoost model.

[0127] Using the refined AdaBoost model, 11 genes were identified as statistically associated with preeclampsia. These genes include CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH and NES. The performance of this predictive model was validated using a holdout dataset from RGH14 as well as in a completely independent Pearl Biobank cohort (Figure 19).

[0128] Description of AdaBoost model generation. The AdaBoost classification method was refined to obtain a more specific set of genes (AdaBoost Refined 1-7) by the following procedure, also shown in Figure 18. The RGH14 dataset was split into six pieces by random selection: a holdout subset containing 12% of the samples excluded from model building, and five uniformly sized test subsets.

[0129] For each test subset, the training data was assigned as all samples that were neither holdout nor test samples. Gene counts for the test and training samples were TMM normalized in edgeR, and then the training data was standardized to have a mean of 0 and a standard deviation of 1 for each gene. An AdaBoost model with 90 estimators and a learning rate of 1.6 was then fitted to the training data. Feature pruning was then performed by determining the feature importance of each gene in the model and testing the effect of removing estimators with genes with importance below the threshold. The threshold that gave the best performance (as measured by Matthews correlation coefficient for test data classification) with the fewest genes was selected and that model was retained. This process, starting with building the AdaBoost model, was repeated a minimum of 10 times for this data subset.

[0130] After building all 50-plus models for the five test-training subsets, the estimators from all models were combined into a single AdaBoost model. Feature pruning was performed again, this time using the percentage of models that incorporated genes for the threshold, and performance was evaluated using the average negative log loss value for classification of each test subset. The model that obtained the maximum negative log loss value with the fewest genes was selected as the final AdaBoost model.

[0131] AdaBoost Gene List. Through this iteration of the process, slight variations were observed in the genes selected for the final model due to the inherent randomization in the AdaBoost algorithm implementation, but performance for predicting the test data, holdout data, and an independent (Pearl) data set remained high.

[0132] A total of 11 genes were observed in at least one of the 14 AdaBoost Refined models generated: ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, NES, PAPPA2, SKIL, VSIG4 (AdaBoost Refined 1), but no model was generated that included all of them simultaneously.

[0133] Two observed gene sets gave the best performance for classification of the independent data: AdaBoost Refined 2: ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, VSIG4 and AdaBoost Refined 3: ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, VSIG4.

[0134] Four additional gene sets performed nearly as well as AdaBoost Refined 2-3. These were: AdaBoost Refined 4: ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, VSIG4; AdaBoost Refined 5: ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, VSIG4; AdaBoost Refined 6: ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, SKIL; and AdaBoost Refined 7: ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, SKIL.

[0135] Example 5 Identification of C-RNA signatures using transposome-based library generation RGH14 sample was also processed by Illumina Nextera Flex for Enrichment protocol, enriched for whole exome, and sequenced to over 40 million reads.This method is more sensitive and robust with low input, and therefore has a high possibility of identifying additional genes predicting preeclampsia.This data set was run by three analysis methods: standard differential expression analysis (TREAT), jackknife method, and refined Adaboost model.For detailed description of these analysis methods, please refer to Example 3 and Example 4.

[0136] By changing the method for making libraries, the genes detected in all three analysis methods changed.For the TREAT method, 26 genes were identified as differentially enriched in preeclampsia, and most of them also show increased abundance in preeclampsia (see Table 2, DEX analysis, Nextera Flex for Enrichment library making method).These genes include ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, VSIG4.Figure 20 shows the classification of RGH14 samples in this gene list.

[0137] By applying the jackknife analysis method, the selection of the TREAT list was narrowed to 22 genes identified as differentially enriched in preeclampsia. These genes included ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, VSIG4. The improved performance of this list is shown in Figure 20.

[0138] A refined AdaBoost model approach was applied to this data as described in Example 4. Using this method, 24 genes are identified as statistically associated with preeclampsia (Table 3, AdaBoost analysis, Nextera Flex for Enrichment library construction method). These genes include LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMAG3, TIPARP, LRRC26, PHEX, LILRA4, and PER1. The performance of this predictive model is shown in Figure 21.

[0139] Example 6 Circulating transcriptome measurements from maternal blood detect early-onset preeclampsia signatures Molecular tools to non-invasively monitor maternal health from conception to delivery would allow accurate detection of pregnant women at risk for adverse outcomes. Circulating RNA (C-RNA) is released by all tissues into the bloodstream, providing an accessible and comprehensive measure of placental, fetal and maternal health (Koh et al., 2014, Proceedings of the National Academy of Sciences; 111: 7361-7366; and Tsui et al., 2014, Clinical Chemistry; 60: 954-962). Preeclampsia (PE), a common and potentially life-threatening pregnancy complication, originates in the placenta but spreads to a significant maternal component as the disease progresses (Staff et al., 2013, Hypertension; 61: 932-942; and Chaiworapongsa et al., 2014, Nature Reviews Nephrology; 10, 466-480). Yet, biomarkers have shown limited clinical utility (Poon and Nicolaides, 2014, Obstetrics and Gynecology International; 2014:1-11; Zeisler et al., 2016, N Engl J Med; 374:13-22; and Duhig et al., 2018, F1000 Research; 7:242). Assuming that characterization of the circulating transcriptome may identify better biomarkers, C-RNAs were analyzed from 113 pregnant women (40 at the time of early-onset PE diagnosis). Using a novel workflow, we identified differential abundances of 30 transcripts consistent with PE biology and representing placental, fetal, and maternal contributions. Furthermore, we developed a machine learning model and demonstrated that only seven C-RNA transcripts were required to classify PE in two independent cohorts (92-98% accuracy). The global measurement of C-RNA disclosed in this example highlights its usefulness in monitoring both maternal and fetal health and holds great promise for the diagnosis and prediction of at-risk pregnancies.

[0140] Several studies have been initiated to investigate and identify potential biomarkers in C-RNA for various pregnancy complications (Pan et al., 2017, Clinical Chemistry; 63: 1695-1704; Whitehead et al., 2016, Prenatal Diagnosis; 36: 997-1008; Tsang et al., 2017, Proc Natl Acad Sci USA; 114: E7786-E7795; and Ngo et al., 2018, Science; 360: 1133-1136). However, these studies have involved only a small number of patients and are limited to monitoring only a few genes - mostly placental and fetal transcripts. Measurement of the entire circulating transcriptome is difficult to perform because it requires specific upfront sampling and processing to minimize variability and contamination from cell lysis (Chiu et al., 2001, Clinical Chemistry; 47: 1607-1613; and Page et al., 2013, PLoS ONE; 8: e77963). This complex workflow makes large-scale clinical sampling difficult to perform because the tasks required for immediate processing of blood samples are not feasible in many clinics (Marton and Weiner, 2013, BioMed Research International; 2013: 891391). Therefore, in this example, a method is established that allows overnight transport of blood to a processing laboratory where all steps of sample preparation are performed in a controlled environment, providing a scalable platform for clinical trial level evaluation (Figure 22A).

[0141] A pillar of this method is the ability to transport blood overnight to the processing laboratory. C-RNA pregnancy signal was assessed after overnight room temperature transport in several tube types (Figures 26A-26C). Blood stored in EDTA tubes, representative of those used by previous C-RNA studies, showed reduced abundance of pregnancy-associated transcripts and overall instability of the transcriptomic profile (Qin et al., 2013, BMC Research Notes; 6:380). In contrast, Cell-Free DNA BCT (Streck), the primary tube type used for Non-Invasive Prenatal Testing (NIPT), retained signal from placental transcripts and had improved technical reproducibility (Figure 26B) (Medina Diaz et al., 2016, PLoS ONE; 11: e0166354).

[0142] Blood shipping allowed easy collection of an average of 5mL of plasma per patient from a single tube of blood. The difference in C-RNA data quality was evaluated when using variable plasma volumes, and it was determined that using less than 2mL of plasma significantly increased noise and reduced library complexity (Figures 27A and 27B). Therefore, 4mL of plasma was used in this example test to maximize confidence in data quality.

[0143] We demonstrated this novel workflow by repeating a previous study monitoring the C-RNA dynamics of over 10,000 transcripts per healthy pregnant woman from early to late pregnancy. Using 152 consecutively collected samples from 45 healthy pregnant women (Pre-Eclampsia and Growth Restriction Longitudinal Study Control Cohort-PEARL; NCT02379832; Table 5), we identified 156 significantly altered transcripts, the majority of which increased in abundance as pregnancy progressed (Figure 22B). 42% of the altered genes were identified in previous C-RNA studies (Figure 22C) (Koh et al., 2014, Proceedings of the National Academy of Sciences; 111: 7361-7366; and Tsui et al., 2014, Clinical Chemistry; 60: 954-962). Of the 91 transcripts identified in this study alone, 64% are expressed by placental and / or fetal tissues (Figures 22D and 28A-28C). The remaining genes likely reflect maternal responses to pregnancy.

[0144] Test Design In the next phase of the study, the workflow was applied to clinical samples to measure C-RNA changes in PE (iPC, Illumina Preeclampsia Cohort). PE is a heterogeneous disease associated with variable severity, and patient outcomes are based on whether PE presents before 34 weeks of gestation (early onset) or after 34 weeks of gestation (late onset) (Staff et al., 2013, Hypertension; 61: 932-942; Chaiworapongsa et al., 2014, Nature Reviews Nephrology; 10, 466-4803; and Dadelszen et al., 2003, Hypertension in Pregnancy; 22: 143-148). The study focused on the more severe early-onset forms of PE and defined stringent diagnostic criteria with clear inclusion and exclusion requirements (most rigorously excluding individuals with a history of chronic hypertension) to obtain a clean cohort (Table 6) (Nakanishi et al., 2017, Pregnancy Hypertension; 7: 39-43; and Hiltunen et al., 2017, PLoS ONE; 12: e0187729). Maternal characteristics, pregnancy outcomes, and medications used were recorded throughout the study (Table 7). 113 samples were collected across eight locations (Table 8), 40 were collected at the time of PE diagnosis and 73 controls matched for gestational age within 1 week (Figure 23A). All but one woman with PE delivered preterm, compared to 9.5% of controls, confirming that these diagnostic criteria identify individuals severely affected by the disease (Figure 23C).

[0145] All samples were randomly distributed across multiple processing batches and then sequenced to over 40M reads. Standard differential expression analysis using the entire cohort identified 42 altered transcripts, with 37 increased in PE (Figure 24A, blue and orange). However, the high variability observed in genes detected as altered when different subsets of controls were selected for analysis is of concern.

[0146] To address this discrepancy, we incorporated a jackknife method that allows the identification of the most consistently altered genes (Figures 24A and 24B, orange). 1,000 iterations of the differential analysis with randomly selected sample subsets were performed, which allowed the construction of confidence intervals for the p-values ​​associated with each putatively altered transcript (Figure 29A). 12 genes with confidence intervals above 0.05 were excluded (Figure 24B). These genes were not excluded by simply setting thresholds for baseline abundance or biological variation (Figure 29B), but it was observed that these transcripts had lower predictive value (Figure 29C). Hierarchical clustering indicates that these genes are not widely altered in the PE cohort, thus lacking the sensitivity (73%) for accurate classification of this condition (Figure 29D).

[0147] Next, the analysis focused on a refined set of 30 genes, 60% of which had been previously implicated in PE (Namli et al., 2018, Hypertension in Pregnancy; 37:9-17; Than et al., 2018, Frontiers in Immunology; 9:1661; Kramer et al., 2016, Placenta; 37:19-25; Winn et al., 2008, Endocrinology; 150:452-462; and Liu et al., 2018, Molecular Medicine Reports; 18:2937-2944). qPCR analysis confirmed that 19 of the 20 genes were significantly altered in PE (Figure 24C, Table 9). Remarkably, 40% of these genes encode extracellular or secreted protein products. Furthermore, nearly all genes are involved in PE-related processes, including extracellular matrix (ECM) remodeling, gestational age, placental / fetal development, angiogenesis, and hypoxic response (Table 10). 67% of these transcripts were expressed by the placenta and / or fetus (Figure 24D). Cardiovascular and immune functions were well represented in the remaining maternal expressed transcripts (Table 10). Hierarchical clustering of these genes effectively separated PE and control samples with 98% sensitivity and 97% specificity (Figure 24E). Interestingly, clinical data for the two misidentified controls indicated potentially confounding health issues, as suggested by the use of hypertensive medications by these controls (Table 7).

[0148] The ability to cluster a cohort of samples obtained from an independent biobank using genes identified in iPC was evaluated - Pre-Eclampsia and Growth Restriction Longitudinal Study (PEARL; NCT02379832; Figures 23B and 23C, Table 11). This cohort consisted of early-onset PE (diagnosed at <34 weeks); and late-onset PE, along with gestational age-matched controls. Early-onset PE samples were clustered separately from matched controls with 83% sensitivity and 92% specificity, further validating the association of these transcripts (Figure 24F). In contrast, no clustering was observed for late-onset PE and matched control samples (Figure 24G).

[0149] Next, we used the iPC data to build an AdaBoost model for robust classification of PE samples. To generate a generalized machine learning model that can accurately predict new samples, we used a rigorous approach that avoided overfitting with a single dataset, and we validated the final classifier with samples not used for model construction (Figures 30A-30D and 31A-31E). Surprisingly, the final model used only seven genes, three of which have not been reported before (Figure 25A). For the entire iPC cohort, the model classified samples with extremely high accuracy (AUC=0.99, sensitivity=98%, specificity=99%; Figures 25B and 25C, blue). Early-onset PE PEARL samples were also accurately classified (AUC=0.88, sensitivity=100%, specificity=83%; Figures 25B and 25C, pink). Unexpectedly, late-onset PE PEARL samples were also classified with reasonable accuracy (AUC=0.74, sensitivity=75%, specificity=67%; Figures 25B and 25C, green).

[0150] This gene set was highly consistent with the transcripts identified by differential abundance analysis (Figure 25D; Table 10). The classifier relied on both placental and maternally expressed transcripts (Figure 25E). All genes used by the model form protein products that are either extracellular or membrane-bound. Despite the small number of genes selected by AdaBoost, a variety of PE-related functions were observed, particularly cardiovascular function and angiogenesis, immune regulation, fetal development, and ECM remodeling.

[0151] method Prospective clinical sample collection. Pregnant patients were recruited in an Illumina-sponsored clinical trial protocol in compliance with the International Conference on Harmonization of Pharmaceuticals for Human Use for Good Clinical Practice. After informed consent, 20 mL whole blood samples were collected from 40 pregnant women with a diagnosis of preeclampsia before 34 weeks of gestation with severe features defined according to ACOG guidelines (Table 6). Samples from 76 healthy pregnant women were also collected and matched for gestational age to the preeclampsia group. Three control samples developed late preeclampsia after blood collection and were excluded from data analysis. For detailed inclusion and exclusion criteria, see Table 6. Patient history, treatment, and birth outcome information were also recorded (Table 7).

[0152] Patients were recruited across eight different clinical sites, including University of Texas Medical Branch (Galveston, Texas), Tufts Medical Center (Boston, Massachusetts), Columbia University Irving Medical Center (New York, New York), Winthrop University Hospital (Mineola, New York), St. Peter's University Hospital (New Brunswick, New Jersey), Christiana Care (Newark, Del.), Rutgers University Robert Wood Johnson Medical School (New Brunswick, New Jersey), and New York Presbyterian / Queens (New York, New York). Clinical protocols and informed consent were approved by the Institutional Review Board at each clinical site. See Table 8 for patient distribution across clinical sites.

[0153] PEARL validation cohort study design. Illumina collected plasma samples from the Preeclampsia: Growth Restriction Longitudinal study (PEARL; NCT02379832) as an independent validation cohort. Plasma samples were collected after the study was completed. PEARL samples were collected at the Centre hospitalier universitaire de Quebec (CHU de Quebec) with principal investigator Emmanual Bujold, MD, MSc. A group of 45 control and 45 case pregnant women were recruited into the study, and written informed consent was obtained for all patients. Only participants over the age of 18 were eligible, and all pregnant women had singleton pregnancies.

[0154] Preeclampsia group. Criteria for preeclampsia were defined based on the Society of Obstetricians and Gynecologists of Canada (SOGC) June 2014 criteria for preeclampsia, including the inclusion of a gestational age of 20–41 weeks. Blood samples were taken once at the time of diagnosis.

[0155] Control group. Forty-five pregnant women with gestational age 11-13 weeks who were expected to have normal pregnancies were recruited. Each enrolled patient was followed longitudinally with blood drawn at four time points throughout pregnancy until delivery. Control women were divided into three subgroups, and subsequent follow-up blood draws were staggered throughout pregnancy to cover the entire range of gestational age (Table 5).

[0156] The PEARL control sample was used for two purposes: 153 longitudinal samples from 45 individual women were used to monitor placental dynamics throughout pregnancy. Additionally, control samples were selected for comparison with the preeclampsia cohort, matched for gestational age, and used to validate the model.

[0157] Study sample processing. All samples from Illumina prospective collection and PEARL samples were processed similarly by an investigator blinded to pathology. Two tubes of blood per patient were collected into Cell-Free DNA BCT tubes (Streck) according to the manufacturer's instructions. Blood samples were stored and shipped overnight at room temperature and processed within 72 hours. Blood was centrifuged at 1,600×g for 20 minutes at room temperature, and plasma was transferred to a new tube and centrifuged at 16,000×g for an additional 10 minutes to remove residual cells. Plasma was stored at approximately 80° C. until use. Circulating RNA was extracted from 4.5 mL of plasma using the Circulating Nucleic Acid Kit (Qiagen) followed by DNAse I digestion (Thermofisher) according to the manufacturer's instructions.

[0158] cDNA synthesis and library construction. Circulating RNA was fragmented at 94°C for 8 min, followed by random hexamer-primed cDNA synthesis using the Illumina TruSight Tumor 170 Library Prep kit (Illumina). Illumina sequencing library construction was performed according to the TST170 Tumor Library Prep Kit for RNA with the following modifications to accommodate low RNA input. All reactions were reduced to 25% of the original volume, and ligation adapters were used at a 1:10 dilution. Library quality was assessed using a high sensitivity DNA analysis kit on an Agilent Bioanalzyer 2100 (Agilent).

[0159] Whole exome enrichment. Sequencing libraries were quantified using Quant-iT PicoGreen dsDNA Kit (ThermoFisher Scientific), normalized to 200ng input, and pooled into 4 samples per enrichment reaction. Whole exome enrichment was performed according to the TruSeq RNA Access Library Prep guide (Illumina). Additional blocking oligos lacking 5' biotin designed for hemoglobin genes HBA1, HBA2, and HBB were included in the enrichment reaction to reduce enrichment of these genes in the sequencing library. The final enriched libraries were quantified using Quant-IT Picogreen dsDNA Kit (ThermoFisher Scientific), normalized, and pooled for paired-end 50x50 sequencing on an Illumina HiSeq 2000 platform for a minimum depth of 40 million reads per sample.

[0160] Data analysis. All statistical tests were two-sided unless otherwise stated. Non-parametric tests were used when data were not normally distributed. Sequencing reads were mapped to the human reference genome (hg19) by tophat (v2.0.13) and transcript abundance was quantified by featureCounts (subreads-1.4.6) against RefGene coordinates (obtained 10 / 27 / 2014). Tissue expression data were obtained from Body Atlas (CorrelationEngine, BaseSpace, Illumina, Inc) (Kupershmidt, et al., 2010, PLoS ONE 5; 10.1371 / journal.pone.0013066). v genes with expression 2-fold or higher than the median expression across all tissues in either placenta or fetal tissues (brain, liver, lung, and thyroid) were assigned to the appropriate group. Subcellular localization was obtained from UniProt.

[0161] After exclusion of genes with CPM ≦0.5 in less than 25% of samples, differential expression analysis was performed in R (v3.4.2) using edgeR (v3.20.9). Datasets were normalized by the TMM method, and differentially abundant genes were identified by glmTreat test for log fold change ≧1, followed by Bonferroni-Holm p-value correction. The same process was used for each jackknife iteration, with 90% of the samples in each group selected by random sampling without replacement. After 1,000 jackknife iterations, one-sided 95% confidence intervals for gene-wise p-values ​​were calculated with statsmodels (v0.8.0). Hierarchical clustering analysis was performed using squared Euclidean distance and average linkage.

[0162] AdaBoost was performed in python with scikit-learn (v0.19.1, sklearn.ensemble.AdaBoostClassifier). Optimal hyperparameter values ​​(estimator of 90, learning rate of 1.6) were determined by grid search using the Matthews correlation coefficient to quantify performance. The overall AdaBoost model development strategy is shown in Figures 31A-31E. Datasets (TMM normalized log CPM values ​​of genes with CPM ≦0.5 in less than 25% of samples) were standardized (sklearn.preprocessing.StandardScaler) before fitting the classifier. The same scaler fit on the training data was applied to the corresponding test dataset; all five scalers for the five training datasets were averaged for use in the final model. The decision_function scores were used to construct ROC curves and determine sample classification.

[0163] RT-qPCR validation assay and analysis. C-RNA was isolated and converted to cDNA from 2 ml of plasma from 19 randomly selected preeclampsia (PE) and 19 matched control samples. cDNA was preamplified with TaqMan Preamp master Mix (cat: 4488593) for 16 cycles and diluted 10-fold to a final volume of 500 μL. For qPCR, using the manufacturer's instructions, the reaction mixture contained 5 μL of diluted preamplified cDNA, 10 μL of TaqMan Gene Expression master mix (cat: 4369542), 1 μL of TaqMan Probe, and 4 μL of water. For each TaqMan probe (Table 9), three qPCR reactions were performed per diluted cDNA sample and Cq values ​​were determined using Bio-Rad CFX Manager software. To determine gene abundance for each target gene, ΔΔCq = 2^-(target Cq - ref Cq avg ) with five reference gene probes (ref Cq avg ) was used to calculate the mean Cq value between the PE / CTRL and CTRL samples. To determine the fold change (PE / CTRL) for each probe, the ΔΔCq value for each sample was divided by the mean ΔΔCq value for the matched control group.

[0164] Tube type study. To evaluate the impact of tube type and overnight shipping on circulating RNA quality, blood was collected from pregnant and non-pregnant women in the following tube types: K2 EDTA (Beckton Dickinson), ACD (Beckton Dickinson), Cell-Free RNA BCT tube (Streck), and one Cell-Free DNA BCT tube (Streck). 8 mL of blood was collected in each tube and either shipped overnight on ice packs (EDTA and ACD) or at room temperature (Cell-Free RNA and DNA BCT tubes). All shipped blood tubes were processed into plasma within 24 hours of blood collection. As a control, 8 mL of blood was collected in K2 EDTA tubes, processed into plasma in situ within 4 hours, and shipped as plasma on dry ice. All plasma processing and circulating RNA extraction was performed as described in the methods section. 3 mL of plasma per condition was used to generate sequencing libraries for enrichment using the Illumina protocol as described.

[0165] Reproducibility study. Plasma was collected from 10 individuals and divided into volumes of 4 mL, 1 mL, and 0.5 mL, with three replicates for each volume. Circulating RNA extraction (Qiagen Circulating Nucleic Acid Kit) and random primed cDNA synthesis were performed on all samples as described above. For libraries with 4.5 mL plasma input, sequencing libraries were generated using the TST170 Tumor Library Prep Kit as described above. For 1 mL and 0.5 mL input, libraries were generated using the Accel-NGS 1S Plus DNA Library Kit (Swift Biosciences). Whole exome enrichment and sequencing were performed on all samples using as described above.

[0166] explanation This study focused on identifying differences that are widespread in early-onset PE, which aids in the ultimate goal of clinically actionable biomarker discovery. This required adjusting the analysis method to account for the variability observed in the data. This variability comes from both the considerable biological noise in C-RNA measurements as well as the phenotypic diversity of PE. C-RNA is inherently more variable than a single tissue transcriptomics because it represents a combination of cell death, signaling, and gene expression across all organs. Furthermore, PE exhibits a wide range of maternal and fetal outcomes that may be related to various underlying molecular causes. While the genes that are removed may be biologically relevant in PE, they are not widespread in the cohort. Interestingly, the excluded transcripts were increased in certain women that may represent molecular subsets of PE. A larger cohort would help elucidate whether C-RNA may reveal PE subtypes, which is important for understanding the diverse pathophysiology of this condition.

[0167] The broadest set of transcripts was identified by AdaBoost. The success of this method was realized by the highly accurate classification of an independent early-onset PE cohort (PEARL). These samples were taken from various populations with fairly loose inclusion and exclusion criteria, including women in the control group (none of whom were misidentified as having PE) who had chronic hypertension, gestational diabetes, or Alport syndrome. In contrast to hierarchical clustering, 17 out of 24 individuals from the late-onset PE cohort were properly classified by the machine learning model of this example, which was unexpected given the suggestion that early-onset and late-onset PE are distinct conditions. The findings of this example suggest that there may be some pathways that are broadly altered in all PE.

[0168] In all evaluations, C-RNA showed changes in placental, fetal, and maternal expressed transcripts. One of the most striking trends observed in PE samples is the increased abundance of a myriad of ECM remodeling and cell migration / invasion proteins (FAM107A, SLC9A3R2, TIMP4, ADAMTS1, PRG2, TIMP3, LEP, ADAMTS2, ZEB1, HSPA12B) with dysfunctional extravillous trophoblast invasion and remodeling of maternal vascular properties in this disease. The maternal side of early-onset PE manifests as cardiovascular dysfunction, inflammation, and preterm labor (PNMT, ZEB1, CRH), all of which show molecular signatures of abnormal behavior in the data of this example.

[0169] [Table 13]

[0170] [Table 14]

[0171] [Table 15]

[0172] [Table 16]

[0173] [Table 17]

[0174] [Table 18]

[0175] [Table 19]

[0176] The complete disclosures of all patents, patent applications, and publications cited herein, and electronically available materials (including, e.g., nucleotide sequence submissions in GenBank and RefSeq, and amino acid sequence submissions in, e.g., SwissProt, PIR, PRF, PDB, and translations from annotated coding regions in GenBank and RefSeq) are incorporated herein by reference in their entirety. Supplementary materials referenced in publications (such as supplementary tables, figures, materials and methods, and / or experimental data) are likewise incorporated by reference in their entirety. In the event of any inconsistency between the disclosure of this application and the disclosure of any document incorporated herein by reference, the disclosure of this application shall govern. The foregoing detailed description and examples are given only for clarity of understanding. No unnecessary limitations should be understood therefrom. The disclosure is not limited to the exact details shown and described, and variations obvious to one skilled in the art will be included within the disclosure defined by the claims.

[0177] Unless otherwise indicated, all numbers expressing amounts of ingredients, molecular weights, and the like used in the specification and claims should be understood to be modified in all instances by the term "about." Accordingly, unless otherwise indicated, the numerical parameters set forth in the specification and claims are approximations that may vary depending on the desired properties sought to be obtained by the present disclosure. At the very least, and not as an attempt to limit the scope of the claims to the doctrine of equivalents, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0178] Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the disclosure are approximations, the numerical values ​​set forth in the specific examples are reported as precisely as possible, however, all numerical values ​​inherently contain ranges necessarily resulting from the standard deviation found in their respective testing measurements.

[0179] All headings are for the convenience of the reader and should not be used to limit the meaning of the text that follows the heading, unless so specified.

Claims

1. 1. A method for detecting preeclampsia and / or determining an increased risk of preeclampsia in a pregnant woman, comprising: identifying a plurality of circulating RNA (C-RNA) molecules in a biological sample taken from the pregnant woman; The protein coding sequence encoded by the plurality of C-RNA molecules is ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL Including, The method, wherein said plurality of C-RNA molecules is indicative of preeclampsia and / or an increased risk of preeclampsia in a pregnant woman.

2. 1. A method for detecting preeclampsia and / or determining an increased risk of preeclampsia in a pregnant woman, comprising: purifying a population of circulating RNA (C-RNA) molecules from a biological sample taken from the pregnant woman; identifying a protein coding sequence encoded by said C-RNA molecule within said purified population of C-RNA molecules; wherein the protein coding sequence encoded by the C-RNA molecule is ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL Including, wherein said protein coding sequence encoded by said C-RNA molecule is indicative of preeclampsia and / or an increased risk of preeclampsia in said pregnant woman.

3. The method according to claim 1 or 2, wherein the step of identifying the protein-coding sequence encoded by the C-RNA molecule in the biological sample comprises hybridization, reverse transcription PCR, microarray chip analysis, or sequencing.

4. Prior to identifying the protein coding sequence encoded by said circular RNA (C-RNA) molecule, removing intact cells from said biological sample; treating the biological sample with deoxyribonuclease (DNase) to remove cell-free DNA (cfDNA); synthesizing complementary DNA (cDNA) from the C-RNA molecules in the biological sample; and / or Enriching said cDNA sequences for protein-coding DNA sequences by exome enrichment. The method of any one of claims 1 to 3, further comprising:

5. 1. A method for detecting preeclampsia and / or determining an increased risk of preeclampsia in a pregnant woman, comprising: removing intact cells from the biological sample taken from the pregnant woman; treating the biological sample with deoxyribonuclease (DNase) to remove cell-free DNA (cfDNA); synthesizing complementary DNA (cDNA) from RNA molecules in the biological sample; enriching said cDNA sequences for protein-coding DNA sequences (exome enrichment); sequencing the obtained enriched cDNA sequences; Identifying protein-coding sequences encoded by the enriched C-RNA molecules; Including; wherein the protein coding sequence encoded by the C-RNA molecule is ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL Including, A method, wherein a protein coding sequence encoded by said C-RNA molecule is indicative of preeclampsia and / or an increased risk of preeclampsia in said pregnant woman.

6. removing intact cells from a biological sample taken from a pregnant woman; treating the biological sample with deoxyribonuclease (DNase) to remove cell-free DNA (cfDNA); synthesizing complementary DNA (cDNA) from RNA molecules in the biological sample; enriching said cDNA sequences for protein-coding DNA sequences (exome enrichment); sequencing the obtained enriched cDNA sequences; identifying protein coding sequences encoded by the enriched C-RNA molecules; 1. A method comprising: wherein the protein coding sequence encoded by the C-RNA molecule is ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, and VSIG4; ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL A method comprising:

7. 7. The method of claim 3, 5, or 6, wherein sequencing comprises massively parallel sequencing of clonally amplified molecules.

8. The method of claim 3, 5, or 6, wherein the sequencing comprises RNA sequencing.

9. The method of any one of claims 1 to 8, wherein the biological sample comprises plasma.

10. The biological sample comprises: Pregnant women less than 16 weeks pregnant; Pregnant women less than 20 weeks pregnant; or Pregnant women over 20 weeks pregnant The method according to any one of claims 1 to 9, wherein the extract is obtained from

11. 11. The method of any one of claims 1 to 10, wherein the sample is a blood sample, and said blood sample is collected, transported and / or stored in a tube with cell and DNA stabilizing properties prior to processing the blood sample into plasma.

12. The method of claim 11, wherein the tube comprises a Streck Cell-Free DNA BCT® blood collection tube.

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