A METHOD OF DIAGNOSING AND/OR PROGNOSING PREECLAMPSIA

FIELD OF THE INVENTION

The present invention relates to a method of diagnosing and/or prognosing preeclampsia in a subject. The method comprises determining the quantitative level of one or more biomarkers in a biological sample from the subject and either diagnosing preeclampsia, prognosing unstable moderate early-onset preeclampsia, and/or diagnosing preeclampsia and prognosing unstable moderate early-onset preeclampsia in the subject; based on the quantitative level of the or each biomarker in the biological sample.

BACKGROUND TO THE INVENTION

Preeclampsia (PE) is a disorder of pregnancy characterised by the onset of high blood pressure and often a significant amount of protein in the urine. When PE arises, the condition begins after 20 weeks of pregnancy. In severe disease, there may be red blood cell breakdown, a low blood platelet count, impaired liver function, kidney dysfunction, swelling, shortness of breath due to fluid in the lungs, or visual disturbances. PE increases the risk of poor outcomes for both the mother and the baby. If left untreated, it may result in seizures at which point it is known as eclampsia. The cause of PE is still not fully understood, though the disease was recognised and described nearly 2000 years ago. At present, delivery of the pre-term baby is the only treatment for PE and the safest option for the mother.

PE affects 2-8% of pregnancies worldwide and severe cases develop in about 1 to 2% of pregnancies. PE is a leading cause of maternal and infant mortality with nearly 50,000 maternal and 500,000 infant deaths each year worldwide. Hypertensive disorders of pregnancy (which include preeclampsia) are one of the most common causes of death due to pregnancy. Women who have had preeclampsia are at increased risk of heart disease and stroke later in life; and infant prematurity carries increased risk of long- and short-term respiratory conditions, neurodevelopmental impairment and chronic health problems.

PE is diagnosed when a pregnant woman develops:

- 1. blood pressure 140 mmHg systolic or 90 mmHg diastolic on two separate readings taken at least four to six hours apart after 20 weeks' gestation in an individual with previously normal blood pressure; or
- 2. in a woman with essential hypertension beginning before 20 weeks' gestational age, the diagnostic criteria are: an increase in systolic blood pressure (SBP) of 30 mmHg or an increase in diastolic blood pressure (DBP) of mmHg; or
- 3. proteinuria 0.3 grams (300 mg) or more of protein in a 24-hour urine sample or a SPOT urinary protein to creatinine ratio or a urine dipstick reading of 1+ or greater (dipstick reading should only be used if other quantitative methods are not available).

PE is usually classified as mild or severe. In most settings, PE is classified as severe when any of the following conditions are present: severe hypertension, heavy proteinuria or substantial maternal organ dysfunction. PE usually occurs after 32 weeks. However, if it occurs earlier it is associated with worse outcomes. Early onset (before 32-34 weeks of pregnancy) preeclampsia (EOP) and foetal morbidity are used as independent criteria to classify PE as severe in some parts of the world. Maternal deaths can occur among severe cases, but the progression from mild to severe can be rapid, unexpected, and occasionally fulminant.

EOP can be further classified in terms of severity progression, as stable moderate early-onset preeclampsia (SM EOP) and unstable moderate early-onset preeclampsia (UM EOP). Severity of EOP drives a preterm delivery, associated with a significant risk of long-term neurodevelopmental infant morbidity and mortality; and accounts for a huge proportion of annual admissions to neonatal ICU. Even a minor improvement in gestation at delivery would confer a significant infant survival benefit, such that every hour in utero counts. Accurate risk stratification would reduce these enormous competing risks for mothers and babies, facilitating early intervention before severe complications have occurred. On the other hand, accurately identifying EOP which will not progress to a severe phenotype could prevent unnecessary preterm/emergency delivery with all its associated complications for both mothers and babies.

SM EOP is defined as moderate preeclampsia (according to NICE definition) at the time of admission and at delivery (severe preeclampsia according to the ACOG definition but excluding those with diastolic blood pressure (DBP) 90-99 mmHg and systolic blood pressure (SBP) 140-149 mmHg). UM EOP is defined as moderate preeclampsia at the time of admission (as described above), whose condition worsened and ultimately experienced severe preeclampsia (classified if blood pressure exceeds 160/110 mmHg with more pronounced proteinuria or when it is associated with thrombocytopenia (low platelet count), IUGR and/or liver damage) at the time of delivery (excluding those with DBP 90-99 mmHg and SBP 140-149 mmHg on admission).

Diagnosing PE and EOP is further complicated by the fact that signs and symptoms, such as headache and swollen ankles, are commonly seen in pregnancy and overlap with other medical conditions seen in pregnancy, such as liver disease, renal disease, chronic hypertension, and idiopathic thrombocytopenic purpura. Moreover, the standard clinical diagnostic criteria, such as hypertension or proteinuria, are not sufficiently accurate to predict adverse outcomes associated with the disease.

Thus, there is a need to diagnose PE, especially EOP, more effectively/specifically and to prognose SM EOP and UM EOP more effectively/specifically.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention, there is provided a method of diagnosing preeclampsia in a subject, the method comprising the steps of:

- (a) determining the quantitative level of one or more biomarkers in a biological sample from the subject; and
- (b) diagnosing preeclampsia in the subject based on the quantitative level of the or each biomarker in the biological sample;
- wherein the biomarker is FN1.

Optionally or additionally, the method further comprises prognosing unstable moderate early-onset preeclampsia in the subject.

Optionally, there is provided a method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia in a subject, the method comprising the steps of:

- (a) determining the quantitative level of one or more biomarkers in a biological sample from the subject; and
- (b) diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia in the subject based on the quantitative level of the or each biomarker in the biological sample;
- wherein the biomarker is FN1.

According to a second aspect of the present invention, there is provided a method of diagnosing preeclampsia in a subject, the method comprising the steps of:

- (a) determining the quantitative level of one or more biomarkers in a biological sample from the subject; and
- (b) diagnosing preeclampsia in the subject based on the quantitative level of the or each biomarker in the biological sample;
- wherein the biomarker is CPB2.

Optionally or additionally, the method further comprises prognosing stable moderate early-onset preeclampsia in the subject.

Optionally, there is provided a method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia in a subject, the method comprising the steps of:

- (a) determining the quantitative level of one or more biomarkers in a biological sample from the subject; and
- (b) diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia in the subject based on the quantitative level of the or each biomarker in the biological sample;
- wherein the biomarker is CPB2.

Optionally, the or each biomarker for diagnosing preeclampsia, and/or prognosing unstable moderate early-onset preeclampsia is selected from FN1 and CPB2.

Optionally, there is provided a method of diagnosing preeclampsia, and/or prognosing unstable moderate early-onset preeclampsia in a subject, the method comprising the steps of:

- (a) determining the quantitative level of one or more biomarkers in a biological sample from the subject; and
- (b) diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia in the subject based on the quantitative level of the or each biomarker in the biological sample;
- wherein the biomarkers are FN1 and CPB2.

Optionally, there is provided a method of diagnosing preeclampsia wherein the method of diagnosing preeclampsia is early-onset preeclampsia.

Further optionally, the or each biomarker for diagnosing preeclampsia, and/or prognosing unstable moderate early-onset preeclampsia is further selected from ORM2, IGLC2, C5, C9, ENDOD1, FGA, HBD, PSG1, STOM, EHD1 and DNM1L.

Optionally, the or each biomarker for diagnosing preeclampsia, and/or prognosing unstable moderate early-onset preeclampsia is selected from ORM2, IGLC2, C5, C9, CPB2, ENDOD1, FGA, HBD, PSG1, STOM, EHD1 and DNM1L.

Optionally, the or each biomarker for diagnosing preeclampsia, and/or prognosing unstable moderate early-onset preeclampsia is selected from ORM2, IGLC2, C5, C9, ENDOD1, FGA, FN1, HBD, PSG1, STOM, EHD1 and DNM1L.

Optionally, the or each biomarker for diagnosing preeclampsia, and/or prognosing stable moderate early-onset preeclampsia is selected from ORM2, IGLC2, C5, C9, CPB2, ENDOD1, FGA, FN1, HBD, PSG1, STOM, EHD1 and DNM1L.

Optionally, the method is a method of diagnosing preeclampsia in a subject.

Optionally, the method is a method of diagnosing early-onset preeclampsia in a subject.

Optionally, there is provided a method of diagnosing preeclampsia in a subject, the method comprising the steps of:

- (a) determining the quantitative level of one or more biomarkers in a biological sample from the subject; and
- (b) diagnosing preeclampsia in the subject based on the quantitative level of the or each biomarker in the biological sample;
- wherein the biomarker is FN1.

Optionally, the or each biomarker for diagnosing preeclampsia is further selected from CPB2, ENDOD1 and EHD1.

Optionally, the or each biomarker for diagnosing preeclampsia is selected from FN1, CPB2, ENDOD1 and EHD1.

Optionally, the or each biomarker for diagnosing preeclampsia is further selected from C5, PSG1, DNM1L, PRDX2, IGLC2, ORM2, FBLN1, HBD, STOM, FGA and AMBP.

Optionally, the or each biomarker for diagnosing preeclampsia is selected from FN1, CPB2, ENDOD1, EHD1, C5, PSG1, DNM1L, PRDX2, IGLC2, ORM2, FBLN1, HBD, STOM, FGA and AMBP.

Optionally, the or each biomarker for diagnosing preeclampsia is further selected from C3, GUSB, C9, CCT7, PSG2, CFB, HBB, SERPINA1, APOC3, FGG and IGHG2.

Optionally, the or each biomarker for diagnosing preeclampsia is selected from FN1, CPB2, ENDOD1, EHD1, C5, PSG1, DNM1L, PRDX2, IGLC2, ORM2, FBLN1, HBD, STOM, FGA, AMBP, C3, GUSB, C9, CCT7, PSG2, CFB, HBB, SERPINA1, APOC3, FGG and IGHG2.

Optionally, the or each biomarker for diagnosing preeclampsia is selected from FN1, CPB2 ENDOD1, EHD1, C5, PSG1, DNM1L, PRDX2, IGLC2, ORM2, FBLN1, HBD, STOM, FGA, AMBP, GUSB, C9, CCT7, PSG2, CFB, HBB, SERPINA1, APOC3, FGG and IGHG2.

Optionally, the method of diagnosing preeclampsia includes early-onset preeclampsia.

Optionally, the method is a method of prognosing unstable moderate early-onset preeclampsia in a subject.

Optionally, there is provided a method of prognosing unstable moderate early-onset preeclampsia in a subject, the method comprising the steps of:

- (a) determining the quantitative level of one or more biomarkers in a biological sample from the subject; and
- (b) prognosing unstable moderate early-onset preeclampsia in the subject based on the quantitative level of the or each biomarker in the biological sample;
- wherein the or each biomarker is FN1.

Optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is further selected from CPB2, C5, PSG1, DNM1L, IGLC2, HBD and ORM2.

Optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is selected from FN1, CPB2, C5, PSG1, DNM1L, IGLC2, HBD and ORM2

Optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is further selected from ENDOD1, EHD1, STOM, FGA, COL4A2, APOE, PSG9 and C9.

Optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is selected from FN1, CPB2, C5, PSG1, DNM1L, IGLC2, HBD, ORM2 ENDOD1, EHD1, STOM, FGA, COL4A2, APOE, PSG9 and C9.

Optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is further selected from ALB, PRKAR2B, LBP, HPSE, PSG3, SHBG, SVEP1, UBE2L3 and SERPIND1.

Optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is selected from FN1, CPB2, C5, PSG1, DNM1L, PSG1, IGLC2, HBD, ORM2, ENDOD1, EHD1, STOM, FGA, COL4A2, APOE, PSG9, C9, ALB, PRKAR2B, LBP, HPSE, PSG3, SHBG, SVEP1, UBE2L3 and SERPIND1.

Optionally, the or each biomarker is a gene.

Optionally, the or each biomarker is a nucleic acid.

Optionally, the or each biomarker is a deoxyribonucleic acid.

Optionally, the or each biomarker is a ribonucleic acid.

Optionally, the or each biomarker is a protein.

Optionally, the or each biomarker is a peptide.

Optionally, the or each biomarker for diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR, TYLGNALVCTCYGGSR and ESKPLTAQQTTK.

Preferably, the or each biomarker for diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR and TYLGNALVCTCYGGSR for diagnosing preeclampsia.

Preferably, the or each biomarker for diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence ESKPLTAQQTTK for prognosing unstable moderate early-onset preeclampsia.

Further preferably, the or each biomarker for diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence IHIGSSFEK.

Optionally, the or each biomarker for diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR, TYLGNALVCTCYGGSR and IHIGSSFEK for diagnosing preeclampsia; and ESKPLTAQQTTK and IHIGSSFEK for prognosing unstable moderate early-onset preeclampsia.

Optionally, the or each biomarker for diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of NWGLSFYADKPETTK, EQLGEFYEALDCLCIPR, SYSCQVTHEGSTVEK, ATLVCLISDFYPGAVTVAWK, GGSASTWLTAFALR, MVETTAYALLTSLNLK, LSPIYNLVPVK, VVEESELAR, RPWNVASLIYETK, ALTPQCGSGEDLYILTGTVPSDYR, HPDEAAFFDTASTGK, VQHIQLLQK, GLIDEVNQDFTNR, VNVDAVGGEALGR, GTFSQLSELHCDK, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, EASMVITESPAALQLR, VIAAEGEMNASR, VYGALMWSLGK and IFSPNVVNLTLVDLPGMTK.

Further optionally, the or each biomarker for diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of NWGLSFYADKPETTK, EQLGEFYEALDCLCIPR, SYSCQVTHEGSTVEK, ATLVCLISDFYPGAVTVAWK, GGSASTWLTAFALR, MVETTAYALLTSLNLK, LSPIYNLVPVK, VVEESELAR, RPWNVASLIYETK, IHIGSSFEK, ALTPQCGSGEDLYILTGTVPSDYR, HPDEAAFFDTASTGK, VQHIQLLQK, GLIDEVNQDFTNR, FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR, TYLGNALVCTCYGGSR, ESKPLTAQQTTK, VNVDAVGGEALGR, GTFSQLSELHCDK, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, EASMVITESPAALQLR, VIAAEGEMNASR, VYGALMWSLGK and IFSPNVVNLTLVDLPGMTK.

Preferably, the or each biomarker for diagnosing preeclampsia is a peptide having an amino acid sequence selected from any one or more of FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR and TYLGNALVCTCYGGSR.

Further preferably, the or each biomarker for diagnosing preeclampsia is a peptide having an amino acid sequence selected from any one or more of IHIGSSFEK, ALTPQCGSGEDLYILTGTVPSDYR and VYGALMWSLGK.

Optionally, the or each biomarker for diagnosing preeclampsia is a peptide having an amino acid sequence selected from any one or more of GGSASTWLTAFALR, FTFTLHLETPKPSISSSNLNPR, IFSPNVVNLTLVDLPGMTK, LSEDYGVLK, EGGLGPLNIPLLADVTR, SYSCQVTHEGSTVEK, NWGLSFYADKPETTK, ATLVCLISDFYPGAVTVAWK, AITPPHPASQANIIFDITEGNLR, VNVDAVGGEALGR, EASMVITESPAALQLR, HPDEAAFFDTASTGK, ETLLQDFR and AFIQLWAFDAVK.

Optionally, the or each biomarker for diagnosing preeclampsia is a peptide having an amino acid sequence selected from any one or more of FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR, TYLGNALVCTCYGGSR, IHIGSSFEK, ALTPQCGSGEDLYILTGTVPSDYR, VYGALMWSLGK, GGSASTWLTAFALR, FTFTLHLETPKPSISSSNLNPR, IFSPNVVNLTLVDLPGMTK, LSEDYGVLK, EGGLGPLNIPLLADVTR, SYSCQVTHEGSTVEK, NWGLSFYADKPETTK, ATLVCLISDFYPGAVTVAWK, AITPPHPASQANIIFDITEGNLR, VNVDAVGGEALGR, EASMVITESPAALQLR, HPDEAAFFDTASTGK, ETLLQDFR and AFIQLWAFDAVK.

Further optionally, the or each biomarker for diagnosing preeclampsia is a peptide having an amino acid sequence selected from any one or more of SSLSVPYVIVPLK, IPIEDGSGEVVLSR, SLLEQYHLGLDQK, LSPIYNLVPVK, SLHDAIMIVR, NSATGEESSTSLTVK, SDPVTLNLLHGPDLPR, DFHINLFQVLPWLK, GTFATLSELHCDK, LYHSEAFTVNFGDTEEAK, GWVTDGFSSLK, IHLISTQSAIPYALR and NQVSLTCLVK.

Still further optionally, the or each biomarker for diagnosing preeclampsia is a peptide having an amino acid sequence selected from any one or more of FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR, TYLGNALVCTCYGGSR, IHIGSSFEK, ALTPQCGSGEDLYILTGTVPSDYR, VYGALMWSLGK, GGSASTWLTAFALR, FTFTLHLETPKPSISSSNLNPR, IFSPNVVNLTLVDLPGMTK, LSEDYGVLK, EGGLGPLNIPLLADVTR, SYSCQVTHEGSTVEK, NWGLSFYADKPETTK, ATLVCLISDFYPGAVTVAWK, AITPPHPASQANIIFDITEGNLR, VNVDAVGGEALGR, EASMVITESPAALQLR, HPDEAAFFDTASTGK, ETLLQDFR, IHIGSSFEK AFIQLWAFDAVK, SSLSVPYVIVPLK, IPIEDGSGEVVLSR, SLLEQYHLGLDQK, LSPIYNLVPVK, SLHDAIMIVR, NSATGEESSTSLTVK, SDPVTLNLLHGPDLPR, DFHINLFQVLPWLK, GTFATLSELHCDK, LYHSEAFTVNFGDTEEAK, GWVTDGFSSLK, IHLISTQSAIPYALR and NQVSLTCLVK.

Preferably, the biomarker for prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence ESKPLTAQQTTK.

Further preferably, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of IHIGSSFEK, GGSASTWLTAFALR, MVETTAYALLTSLNLK, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, IFSPNVVNLTLVDLPGMTK, SYSCQVTHEGSTVEK, GTFSQLSELHCDK and EQLGEFYEALDCLCIPR.

Optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of ESKPLTAQQTTK, IHIGSSFEK, GGSASTWLTAFALR, MVETTAYALLTSLNLK, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, IFSPNVVNLTLVDLPGMTK, SYSCQVTHEGSTVEK, GTFSQLSELHCDK and EQLGEFYEALDCLCIPR.

Further optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of ALTPQCGSGEDLYILTGTVPSDYR, VYGALMWSLGK, VIAAEGEMNASR, VQHIQLLQK, SVSIGYLLVK, VQAAVGTSAAPVPSDNH, SNPVILNVLYGPDLPR, GLIDEVNQDFTNR, VVEESELAR and RPWNVASLIYETK.

Further optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of IHIGSSFEK, GGSASTWLTAFALR, MVETTAYALLTSLNLK, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, IFSPNVVNLTLVDLPGMTK, SYSCQVTHEGSTVEK, GTFSQLSELHCDK, EQLGEFYEALDCLCIPR, ALTPQCGSGEDLYILTGTVPSDYR, VYGALMWSLGK, VIAAEGEMNASR, VQHIQLLQK, SVSIGYLLVK, VQAAVGTSAAPVPSDNH, SNPVILNVLYGPDLPR, GLIDEVNQDFTNR, VVEESELAR and RPWNVASLIYETK.

Still further optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of DVFLGMFLYEYAR, LVRPEVDVMCTAFHDNEETFLK, AATITATSPGALWGLDR, ITLPDFTGDLR, TDFLIFDPK, LFIPQITTK, DIPQPHAEPWAFSLDLGLK, LLSDFPVVPTATR, IEINFPAEYPFKPPK and FPVEMTHNHNFR.

Still further optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is a peptide having an amino acid sequence selected from any one or more of ESKPLTAQQTTK, IHIGSSFEK, GGSASTWLTAFALR, MVETTAYALLTSLNLK, FTFTLHLETPKPSISSSNLNPR, IFSPNVVNLTLVDLPGMTK, LPKPYITINNLNPR, SYSCQVTHEGSTVEK, GTFSQLSELHCDK, EQLGEFYEALDCLCIPR, ALTPQCGSGEDLYILTGTVPSDYR, VYGALMWSLGK, VIAAEGEMNASR, VQHIQLLQK, SVSIGYLLVK, VQAAVGTSAAPVPSDNH, SNPVILNVLYGPDLPR, GLIDEVNQDFTNR, VVEESELAR, RPWNVASLIYETK, DVFLGMFLYEYAR, LVRPEVDVMCTAFHDNEETFLK, AATITATSPGALWGLDR, ITLPDFTGDLR, TDFLIFDPK, LFIPQITTK, DIPQPHAEPWAFSLDLGLK, LLSDFPVVPTATR, IEINFPAEYPFKPPK and FPVEMTHNHNFR.

Still further optionally, the or each biomarker for prognosing unstable moderate early-onset preeclampsia is selected from VQAAVGTSAAPVPSDNH, GGSASTWLTAFALR, MVETTAYALLTSLNLK, SVSIGYLLVK, IHIGSSFEK, IFSPNVVNLTLVDLPGMTK, ITLPDFTGDLR, AATITATSPGALWGLDR, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, LFIPQITTK and LLSDFPVVPTATR.

Optionally, the determining step (a) comprises determining the quantitative level of one or more biomarkers in the biological sample from the subject.

Further optionally, the determining step (a) comprises determining the quantitative level of two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, twenty, twenty five, thirty or more biomarkers in the biological sample from the subject.

Optionally, the determining step (a) comprises determining the quantitative level of all of the biomarkers in the biological sample from the subject.

Optionally or additionally, the determining step (a) comprises determining the quantitative level of each of the biomarkers in the biological sample from the subject.

Optionally, the diagnosing and/or prognosing step (b) comprises comparing the quantitative level of the or each biomarker in the biological sample from the subject with the quantitative level of the or each respective biomarkers in a normal sample.

Optionally, in the method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia, the biological sample is selected from platelet releasates, whole blood, serum, plasma, urine, interstitial fluid, peritoneal fluid, cervical swab, tears, saliva, buccal swab, skin, brain tissue, and cerebrospinal fluid.

Preferably, in the method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia, the biological sample is selected from platelet releasates and plasma.

Further preferably, in the method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia, the biological sample is plasma.

Further optionally, in the method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia, the normal sample is a biological sample from a subject not suffering from preeclampsia.

Still further optionally, in the method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia, the normal sample is a biological sample from a healthy pregnant subject.

Optionally, in the method of diagnosing preeclampsia, the biological sample is selected from platelet releasates, whole blood, serum, plasma, urine, interstitial fluid, peritoneal fluid, cervical swab, tears, saliva, buccal swab, skin, brain tissue, and cerebrospinal fluid.

Preferably, in the method of diagnosing preeclampsia, the biological sample is selected from platelet releasates and plasma.

Further preferably, in the method of diagnosing preeclampsia, the biological sample is plasma.

Further optionally, in the method of diagnosing preeclampsia, the normal sample is a biological sample from a subject not suffering from preeclampsia.

Still further optionally, in the method of diagnosing preeclampsia, the normal sample is a biological sample from a healthy pregnant subject.

Optionally, in the method of prognosing unstable moderate early-onset preeclampsia, the biological sample is selected from platelet releasates, whole blood, serum, plasma, urine, interstitial fluid, peritoneal fluid, cervical swab, tears, saliva, buccal swab, skin, brain tissue, and cerebrospinal fluid.

Preferably, in the method of prognosing unstable moderate early-onset preeclampsia, the biological sample is selected from platelet releasates and plasma.

Further preferably, in the method of prognosing unstable moderate early-onset preeclampsia, the biological sample is plasma.

Further optionally, in the method of prognosing unstable moderate early-onset preeclampsia, the normal sample is a biological sample from a subject not suffering from preeclampsia.

Still further optionally, in the method of prognosing unstable moderate early-onset preeclampsia, the normal sample is a biological sample from a healthy pregnant subject.

Optionally, the determining step (a) further comprises determining the quantitative level in a first set of biomarkers.

Optionally, the determining step (a) further comprises determining the quantitative level in a first set of biomarkers wherein the quantitative level of the first set of biomarkers greater than the quantitative level of the respective biomarkers in a normal sample is indicative of preeclampsia.

Optionally, the first set of biomarkers is selected from a peptide having an amino acid sequence selected from any one or more of FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR, TYLGNALVCTCYGGSR, VYGALMWSLGK, GGSASTWLTAFALR, LSEDYGVLK, EGGLGPLNIPLLADVTR, NWGLSFYADKPETTK, AITPPHPASQANIIFDITEGNLR, VNVDAVGGEALGR, EASMVITESPAALQLR, HPDEAAFFDTASTGK, ETLLQDFR, AFIQLWAFDAVK, SSLSVPYVIVPLK, SLHDAIMIVR, NSATGEESSTSLTVK, GTFATLSELHCDK, LYHSEAFTVNFGDTEEAK, GWVTDGFSSLK and IHLISTQSAIPYALR.

Further optionally, the first set of biomarkers is selected from any one or more of FN1, EHD1, C5, PRDX2, ORM2, FBLN1, HBD, STOM, FGA, AMBP, C3, CCT7, PSG2, HBB, SERPINA1, APOC3 and FGG.

Alternatively, the determining step (a) further comprises determining the quantitative level in a second set of biomarkers.

Alternatively, the determining step (a) further comprises determining the quantitative level in a second set of biomarkers wherein the quantitative level of the second set of biomarkers less than the quantitative level of the respective biomarkers in a normal sample is indicative of preeclampsia.

Optionally, the second set of biomarkers is selected from a peptide having an amino acid sequence selected from any one or more of IHIGSSFEK, ALTPQCGSGEDLYILTGTVPSDYR, FTFTLHLETPKPSISSSNLNPR, IFS PNVVNLTLVDLPGMTK, SYSCQVTHEGSTVEK, ATLVCLISDFYPGAVTVAWK, IPIEDGSGEVVLSR, SLLEQYHLGLDQK, LSPIYNLVPVK, SDPVTLNLLHGPDLPR, DFHINLFQVLPWLK and NQVSLTCLVK.

Further optionally, the second set of biomarkers is selected from any one or more of CPB2, ENDOD1, PSG1, DNM1L, IGLC2, C3, GUSB, C9, PSG2, CFB and IGHG2.

Further optionally, the determining step (a) further comprises determining the quantitative level in a third set of bio markers.

Further optionally, the determining step (a) further comprises determining the quantitative level in a third set of biomarkers wherein the quantitative level of the third set of biomarkers greater than the quantitative level of the respective biomarkers in a normal sample is indicative of unstable moderate early-onset preeclampsia.

Optionally, the third set of biomarkers is selected from a peptide having an amino acid sequence selected from any one or more of GGSASTWLTAFALR, MVETTAYALLTSLNLK, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, SYSCQVTHEGSTVEK, EQLGEFYEALDCLCIPR, VYGALMWSLGK, SVSIGYLLVK, VQAAVGTSAAPVPSDNH, SNPVILNVLYGPDLPR, RPWNVASLIYETK, DVFLGMFLYEYAR, LVRPEVDVMCTAFHDNEETFLK, AATITATSPGALWGLDR, ITLPDFTGDLR and FPVEMTHNHNFR.

Further optionally, the third set of biomarkers is selected from any one or more of C5, PSG1, IGLC2, ORM2, EHD1, COL4A2, APOE, PSG9, C9, ALB, PRKAR2B, LBP and SERPIND1.

Further alternatively, the determining step (a) further comprises determining the quantitative level in a fourth set of biomarkers

Further alternatively, the determining step (a) further comprises determining the quantitative level a fourth set of biomarkers wherein the quantitative level of the fourth set of biomarkers less than the quantitative level of the respective biomarkers in a normal sample is indicative of unstable moderate early-onset preeclampsia.

Optionally, the fourth set of biomarkers is selected from a peptide having an amino acid sequence selected from any one or more of ESKPLTAQQTTK, IHIGSSFEK, IFSPNVVNLTLVDLPGMTK, GTFSQLSELHCDK, ALTPQCGSGEDLYILTGTVPSDYR, VIAAEGEMNASR, VQHIQLLQK, GLIDEVNQDFTNR, VVEESELAR, TDFLIFDPK, LFIPQITTK, DIPQPHAEPWAFSLDLGLK, LLSDFPVVPTATR and IEINFPAEYPFKPPK.

Further optionally, the fourth set of biomarkers is selected from any one or more of FN1, CBP2, DNM1L, HBD, ENDOD1, STOM, FGA, C9, HPSE, PSG3, SHBG, SVEP1 and UBE2L3.

Optionally, the method of diagnosing preeclampsia is early-onset preeclampsia.

Optionally, the method of prognosing unstable moderate early-onset preeclampsia further comprises prognosing stable moderate early-onset preeclampsia.

Optionally, the method of prognosing unstable moderate early-onset preeclampsia further comprises differentiating unstable moderate early-onset preeclampsia from stable moderate early-onset preeclampsia.

Optionally, the method of diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia is an in vitro method.

Optionally, the method of diagnosing preeclampsia and/or prognosing unstable moderate preeclampsia is an in vitro method wherein the preeclampsia is early-onset preeclampsia.

Optionally, the method of diagnosing preeclampsia is an in vitro method.

Optionally, the method of diagnosing early-onset preeclampsia is an in vitro method.

Optionally, the method of prognosing unstable moderate early-onset preeclampsia is an in vitro method.

According to a further aspect of the present invention, there is provided a method of treating preeclampsia and/or unstable moderate early-onset preeclampsia by diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia; and treating the subject.

Optionally, the method of treating preeclampsia and/or unstable moderate early-onset preeclampsia by diagnosing preeclampsia and/or prognosing unstable moderate early-onset preeclampsia includes early-onset preeclampsia.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described with reference to the following non-limiting examples and the accompanying drawings, in which:

FIG. 1 illustrates mass spectrometry analysis of pregnancy and early-onset preeclampsia (EOP) platelet releasate;

FIG. 2 illustrates that 36 peptides are differentially expressed in EOP patients;

FIG. 3 illustrates that 36 differentially expressed peptides are capable of 100% separation of patients into healthy pregnancy or EOP group;

FIG. 4 illustrates that 30 peptides are differentially expressed in UM EOP patients;

FIG. 5 illustrates that 30 differentially expressed peptides are capable of 100% separation of patients into SM EOP or UM EOP; and FIG. 6 illustrates plasma expression of the PR biomarkers, wherein the top biomarkers were chosen to confirm their presence in the plasma of EOP patients, wherein both C5 and CPB2 were quantified in the plasma of healthy pregnant and all EOP patients including stable moderate, unstable moderate and severe EOP patients.

Materials & Methods

Patient Recruitment

Following an informed consent, patients were recruited from the Rotunda Hospital in Dublin. Early-onset preeclampsia (EOP) was diagnosed before 34 weeks of gestation with a new-onset hypertension above 140/90 mmHg measured on two separate occasions, proteinuria 0.3 g/24 hrs or 1+ on a dipstick test, low platelet count, IUGR presence and the evidence of abnormal blood flow. Patients were classified as severe EOP if blood pressure exceeded 160/110 mmHg with proteinuria 1.5 g/24 hrs (or 2+ on a dipstick test). Patients were classified into 3 groups based on their disease progression:

- 1. patients with moderate preeclampsia at the time of admission and at delivery but excluding those with diastolic blood pressure (DBP) 90-99 mmHg and systolic blood pressure (SBP) 140-149 mmHg); termed stable moderate early onset preeclampsia (SM EOP);
- 2. patients with severe preeclampsia at the time of admission and delivery; termed stable severe preeclampsia (SS EOP); and
- 3. patients with moderate preeclampsia at the time of admission, whose condition worsened and ultimately experienced severe PE at the time of delivery (excluding those with DBP 90-99 mmHg and SBP 140-149 mmHg on admission); termed unstable moderate preeclampsia (UM EOP).

Platelet Releasate Isolation

Human platelets were obtained from 18 healthy pregnant and 22 early-onset preeclampsia patients in accordance with approved guidelines from University College Dublin and the Rotunda Hospital,

Dublin. Human platelet releasate was isolated from washed platelets. Blood was drawn from human volunteers free from medication into 0.15% v/v acid/citrate/dextrose (ACD) anticoagulant (38 mM anhydrous citric acid, 75 mM sodium citrate, 124 mM D-glucose). Blood was centrifuged at 150 g for 10 minutes at room temperature and platelet rich plasma (PRP) aspirated. Platelets were pelleted from PRP by centrifugation at 720 g for 10 min at room temperature and resuspended in a modified Tyrodes buffer (JNL) (130 mM NaCl, 10 mM trisodium citrate, 9 mM NaHCO₃, 6 mM dextrose, 0.9 mM MgCl₂, 0.81 mM KH₂PO₄, 10 mM Tris pH 7.4) and supplemented with 1.8 mM CaCl₂. Platelet counts were adjusted to 2×10⁸/mL using a Sysmex™ haematology analyser (TOA Medical Electronics, Kobe, Japan). Platelets were activated with 1 U/ml thrombin (Roche, Basel, Switzerland) under constant stirring (1000 rpm) for 5 mins using a Chronolog-700 platelet aggregometer (Chronolog Cor, Manchester, UK). Platelet aggregates were removed by centrifugation by centrifugation three times at 10,000×g for 10 minutes. Harvested platelet releasates were stored at −80° C. until subsequent use.

Sample Preparation and Mass Spectrometry

PR samples were solubilised in RIPA buffer and proteins precipitated overnight with 95% acetone (4:1 acetone: sample volume) at −20° C. Dried protein pellets were resuspended in 8M urea/24 mM Tris-HCL, pH 8.2, at 37° C. for one hour. Disulphide bonds were reduced with 5 mM DTT and protected with 15 mM iodoacetamide. PR samples were digested with Lys-C(1:100; Promega, Madison, Wis.) followed by digestion with trypsin (1:100; Promega). Peptides were purified using ZipTipC₁₈pipette tips (Millipore, Billerica, Mass., USA) and resuspended in 1% formic acid. For data-dependent acquisition (DDA), samples were analysed using a Thermo-Scientific Q-Exactive mass spectrometer connected to a Dionex Ultimate 3000 (RSLCnano) liquid chromatography (LC) system. In brief, each 5 μg sample was loaded onto a fused silica emitter (75 μm ID), pulled using a laser puller (Sutter Instruments P2000, Novato, Calif., USA), packed with Reprocil Pur (Dr Maisch, Ammerbuch-Entringen, Germany) C18 (1.9 μm; 12 cm in length) reverse-phase media and separated by an increasing acetonitrile gradient over 47 minutes (flow rate=250 nUmin) direct into a Q-Exactive MS. The MS was operated in positive ion mode with a capillary temperature of 320° C., and with a potential of 2300 V applied to the frit. All data was acquired while operating in automatic data-dependent switching mode. A high resolution (70,000) MS scan (300-1600 m/z) was performed using the Q Exactive to select the 12 most intense ions prior to MS/MS analysis using high-energy collision dissociation (HCD).

The data-independent acquisition (DIA) isolation scheme and multiplexing strategy was based on that from Egertson et al., 2013 in which five 4-m/z isolation windows are analysed per scan. DIA data were acquired for the psychotic experiences group (see Table 1; 40Cases/66 Controls). Samples were run on the Thermo Scientific Q Exactive mass spectrometer in DIA mode. Each DIA cycle contained one full MS-SIM scan and 20 DIA scans covering a mass range of 490-910^Thwith the following settings: the SIM full scan resolution was 35,000; AGC 1e6; Max IT 55 ms; profile mode; DIA scans were set at a resolution of 17,000; AGC target 1e5; Max IT 20 ms; loop count 10; MSX count 5; 4.0 m/z isolation windows; centroid mode (Egertson et al., 2013). The cycle time was 2s, which resulted in at least ten scans across the precursor peak. For the library, QC samples were injected in DDA mode at the beginning of the run, and after every ten injections throughout the run. The relative fragment-ion intensities, peptide-precursor isotope peaks and retention time of the extracted ion chromatograms from the DIA files were used to confirm the identity of the target molecular species.

Protein Identification and Quantification

Raw DDA MS files were analysed by MaxQuant (MQ) version 1.5.0.30. MS/MS spectra were searched by the Andromeda search engine against a human FASTA (August 2016) obtained from UniProt. MQ analysis included an initial search with a precursor mass tolerance of 20 ppm the results of which were used for mass recalibration. In the main Andromeda search precursor mass and fragment mass had an initial mass tolerance of 6 ppm and 20 ppm, respectively. The search included fixed modification of carbamidomethyl cysteine. Minimal peptide length was set to 7 amino acids and a maximum of 2 miscleavages was allowed. The false discovery rate (FDR) was set to 0.01 for peptide/protein identification. For quantitative comparison between samples we used label-free quantification (LFQ) with a minimum of two ratio counts to determine the normalized protein intensity. LFQ intensities were assigned to identified proteins by comparing the area under the curve of the signal intensity for any given peptide. The total protein approach (TPA) was used for quantitative comparison, to determine protein abundance as a fraction of total protein.

All DIA data were processed in the open-source Skyline software tool (open-source Skyline software tool (https://skyline.gs.washington.edu). This tool provided the interface for visual confirmation of protein biomarkers in the samples profiled, without any file conversion. The library was constructed from previously described MaxQuant analysis. As detailed in the online tutorials and publications by the Skyline team, the msms.txt file resulting from the MaxQuant search was used to build the library in Skyline. For our peptide targets, mass chromatograms were extracted for +2 and +3 precursor charge states and their associated fragment ions. Based on our discovery results, we analysed 89 protein candidates. For our dataset, the m/z tolerance was <10 ppm and the average retention time window was 2 minutes. All parent and fragment level data was visually confirmed across the samples run, and peak editing was undertaken where necessary, using the peptide Retention Time (RT), dotproduct (idop), mass accuracy (<10 ppm), and a confirmed library match to reliably identify and quantify peptides across the DIA runs. For statistical analysis, peak areas of the fragment level data was filtered from the Skyline document grid for analysis in mapDIA, an open source bioinformatics tool for pre-processing and quantitative analysis of DIA data. Retention time normalisation procedure was applied, followed by peptide fragment selection using 2 standard deviation threshold for outlier detection, in the independent sample setup. Differential expression analysis was analysed in Perseus software.

Data were processed in the Perseus open framework (http://www.perseus-framework.org). Protein IDs were filtered to eliminate identifications from the reverse database, proteins only identified by site, and common contaminants. TPA values were Log₂transformed. A protein was included if it was identified in at least 50% of samples in at least one group. Random forest analysis with either 80:20% or 60:40% data split was performed using Waikato Environment for Knowledge Analysis (WEKA).

EXAMPLE 1

Mass Spectrometry Analysis of Pregnancy and Early-Onset Preeclampsia (EOP) Platelet Releasate Exhibits Very Good Correlation

Referring to FIG. 1(A), strong correlations (>0.8) were found in the 18 healthy pregnancies and 22 EOP platelet releasate (PR) samples. FIG. 1(B) shows a table listing average, maximum and minimum correlations as well as standard deviation for 18 healthy pregnancy and 22 EOP PR samples. FIG. 1(C) illustrates pregnancy and EOP platelet releasate proteomes show little variation with an average coefficient of variation (CV) 5.8%±2.5% and 5.9%±2.6% respectively. The maximum variance was lower for the EOP PR (CV=21.9%) when compared to pregnancy PR (CV=24.6%).

From our discovery (DDA) mass spectrometry analysis we have uncovered 89 protein candidates to analyse through DIA. For 9 of 89 proteins we were not able to identify MS1 or MS2 spectra through our DIA methodology and further 2 did not pass DIA inclusion criteria, therefore these were removed from further analysis. Table 1 contains the complete list (78) of the proteins analysed.

TABLE 1

A list of proteins analysed through Skyline software

following data-independent acquisition method

Gene name
Protein name
UniProt ID

FN1
Fibronectin
P02751

C5
Complement C5
P01031

PSG1
Pregnancy-specific beta-1-glycoprotein 1
P11464

PRDX2
Peroxiredoxin-2
P32119

DNM1L
Dynamin-1-like protein
O00429

ENDOD1
Endonuclease domain-containing 1 protein
O94919

CFHR1
Complement factor H-related protein 1
Q03591

ORM2
Alpha-1-acid glycoprotein 2
P19652

IGLC2
Immunoglobulin lambda constant 2
P0DOY2

FBLN1
Fibulin-1
P23142

COL4A2
Collagen alpha-2
P08572

HBD
Hemoglobin subunit delta
P02042

APOE
Apolipoprotein E
P02649

STOM
Erythrocyte band 7 integral membrane protein
P27105

AMBP
Protein AMBP [Cleaved into: Alpha-1-microglobulin
P02760

PSG9
Pregnancy-specific beta-1-glycoprotein 9
Q00887

EHD1
EH domain-containing protein 1
Q9H4M9

FGA
Fibrinogen alpha chain [Cleaved into: Fibrinopeptide
P02671

A; Fibrinogen alpha chain]

C3
Complement C3
P01024

C7
Complement component C7
P10643

CPB2
Carboxypeptidase B2
Q96IY4

GUSB
Beta-glucuronidase
P08236

CCT7
T-complex protein 1 subunit eta
Q99832

IGHV5-10-1
Immunoglobulin heavy variable 5-10-1
A0A0J9YXX1

PSG2
Pregnancy-specific beta-1-glycoprotein 2
P11465

CFB
cDNA FLJ55673, highly similar to Complement
B4E1Z4

factor B

ALB
Serum albumin
P02768

HBB
Hemoglobin subunit beta
P68871

PRKAR2B
cAMP-dependent protein kinase type II-beta
P31323

regulatory subunit

LBP
Lipopolysaccharide-binding protein
P18428

C9
Complement component C9 [Cleaved into:
P02748

Complement component C9a; Complement

component C9b]

HBA1
Hemoglobin subunit alpha
P69905

SERPINA1
Alpha-1-antitrypsin
P01009

HPSE
Heparanase
Q9Y251

PSG3
Pregnancy-specific beta-1-glycoprotein 3
Q16557

TGFBI
Transforming growth factor-beta-induced protein ig-
Q15582

h3

SHBG
Sex hormone-binding globulin
P04278

IGHV3-33
Immunoglobulin heavy variable 3-33
P01772

SVEP1
Sushi, von Willebrand factor type A, EGF and
Q4LDE5

pentraxin domain-containing protein 1

CP
Ceruloplasmin
P00450

CLEC3B
Tetranectin
P05452

UBE2L3
Ubiquitin-conjugating enzyme E2 L3
P68036

SERPIND1
Heparin cofactor 2
P05546

APOC3
Apolipoprotein C-III
P02656

NUTF2
Nuclear transport factor 2
P61970

CTSC
Dipeptidyl peptidase 1
P53634

AGT
Angiotensinogen
P01019

AHSG
Alpha-2-HS-glycoprotein
P02765

FGG
Fibrinogen gamma chain
P02679

PRG2
Bone marrow proteoglycan
P13727

PDCD6IP
Programmed cell death 6-interacting protein
Q8WUM4

IGHG2
Immunoglobulin heavy constant gamma 2
P01859

MMRN1
Multimerin-1
Q13201

ST6GAL1
Beta-galactoside alpha-2,6-sialyltransferase 1
P15907

VTN
Vitronectin
P04004

APOC2
Apolipoprotein C-II
P02655

C1S
Complement C1s subcomponent
P09871

CAND1
Cullin-associated NEDD8-dissociated protein 1
Q86VP6

CD84
SLAM family member 5
Q9UIB8

CSH2
Chorionic somatomammotropin hormone 2
P0DML3

DMTN
Dematin
Q08495

EEF1A1P5
Putative elongation factor 1-alpha-like 3
Q5VTE0

F12
Coagulation factor XII
P00748

FETUB
Fetuin-B
Q9UGM5

GANAB
Neutral alpha-glucosidase AB
Q14697

HINT1
Histidine triad nucleotide-binding protein 1
P49773

HRG
Histidine-rich glycoprotein
P04196

IGKV2-24
Immunoglobulin kappa variable 2-24
A0A0C4DH68

ITIH3
Inter-alpha-trypsin inhibitor heavy chain H3
Q06033

KNG1
Kininogen-1
P01042

MMP1
Interstitial collagenase
P03956

ORM1
Alpha-1-acid glycoprotein 1
P02763

PF4
Platelet factor 4
P02776

PLG
Plasminogen
P00747

PSG7
Putative pregnancy-specific beta-1-glycoprotein 7
Q13046

PZP
Pregnancy zone protein
P20742

SERPINA7
Thyroxine-binding globulin
P05543

UGP2
UTP--glucose-1-phosphate uridylyltransferase
Q16851

Skyline software allows for an analysis of individual peptides identified for each of the target proteins. Utilising mapDIA software, the area under the peak for each peptide fragments was used to assign quantitative value to a corresponding peptide. Furthermore, the quantitative data was normalised to retention time. Missing values were handled by imputation of a constant number equal to the lowest value ×0.9 for any given peptide. The quantitative peptide data was subjected to statistical analysis in Perseus software.

EXAMPLE 2

Peptide Expression in EOP Patients

A peptide level analysis was carried out to assess the diagnosis of EOP further. 207 peptides from 60 proteins were quantified by mapDIA and were subjected to statistical analysis. Student's t-test analysis with an FDR of 5% and S₀of 0.1 (denoted by the black hyperbolic lines, FIG. 2) was performed on the 71 peptides meeting the minimal difference criteria (>0.5/<−0.5). 36 peptides were found differentially expressed in EOP samples, with 12 peptides decreased (right; IHIGSSFEK, ALTPQCGSGEDLYILTGTVPSDYR, FTFTLHLETPKPSISSSNLNPR, IFSPNVVNLTLVDLPGMTK, SYSCQVTHEGSTVEK, ATLVCLISDFYPGAVTVAWK, IPIEDGSGEVVLSR, SLLEQYHLGLDQK, LSPIYNLVPVK, SDPVTLNLLHGPDLPR, DFHINLFQVLPWLK, NQVSLTCLVK) and 24 peptides increased (left; FGFCPMAAHEEICTTNEGVMYR, YSFCTDHTVLVQTR, VDVIPVNLPGEHGQR, EATIPGHLNSYTIK, IYLYTLNDNAR, TYLGNALVCTCYGGSR, VYGALMWSLGK, GGSASTWLTAFALR, LSEDYGVLK, EGGLGPLNIPLLADVTR, NWGLSFYADKPETTK, AITPPHPASQANIIFDITEGNLR, VNVDAVGGEALGR, EASMVITESPAALQLR, HPDEAAFFDTASTGK, ETLLQDFR, AFIQLWAFDAVK, SSLSVPYVIVPLK, SLHDAIMIVR, NSATGEESSTSLTVK, GTFATLSELHCDK, LYHSEAFTVNFGDTEEAK, GWVTDGFSSLK, IHLISTQSAIPYALR) in EOP.

FIG. 2 shows a volcano plot of healthy pregnancy vs EOP PR proteomes representing the peptides significantly altered in EOP patients. The black hyperbolic curved lines show the threshold for statistical significance, with an FDR of 0.05 and a minimal difference of 0.1. Our analysis revealed that the secretion levels of 24 peptides were significantly increased in EOP PR (circle) whereas 12 peptides were significantly decreased in EOP PR (triangle).

EXAMPLE 3

Peptide separation of healthy pregnant and EOP patients

The hierarchical clustering and principal component analysis of the 36 differential proteins illustrate a complete separation of healthy pregnant and EOP patients (FIG. 3).

FIG. 3(A) illustrates euclidian distance based two-way hierarchical clustering of the log²transformed quantitative values of the 36 peptides (24 increased and 12 decreased) differentially released in EOP PR completely separates the samples into pregnancy and EOP clusters. FIG. 3(B) illustrates principal component analysis of log²transformed quantitative values of the 36 differential peptides. Combination of component 1 and 2 enables complete separation of healthy pregnancy (triangle) and EOP (circle) PR samples.

The peptides deemed differentially expressed in EOP samples were furthermore subjected to machine learning analysis with the WEKA software. The peptides were subjected to InfoGain attribute selection and ranked according to their ability to separate the groups (Table 2).

TABLE 2

Attribute selection ranking for the 36 differential EOP peptides

Rank
Gene names
Peptide
SEQ ID

1
FN1
FGFCPMAAHEEICTTNEGVMYR
SEQ ID NO. 1

1
FN1
YSFCTDHTVLVQTR
SEQ ID NO. 2

1
FN1
VDVIPVNLPGEHGQR
SEQ ID NO. 3

1
FN1
EATIPGHLNSYTIK
SEQ ID NO. 4

1
FN1
IYLYTLNDNAR
SEQ ID NO. 5

1
FN1
TYLGNALVCTCYGGSR
SEQ ID NO. 6

7
CPB2
IHIGSSFEK
SEQ ID NO. 7

8
ENDOD1
ALTPQCGSGEDLYILTGTVPSDYR
SEQ ID NO. 8

9
EHD1
VYGALMWSLGK
SEQ ID NO. 9

10
C5
GGSASTWLTAFALR
SEQ ID NO. 10

11
PSG1
FTFTLHLETPKPSISSSNLNPR
SEQ ID NO. 11

12
DNM1L
IFSPNVVNLTLVDLPGMTK
SEQ ID NO. 12

13
PRDX2
LSEDYGVLK
SEQ ID NO. 13

14
PRDX2
EGGLGPLNIPLLADVTR
SEQ ID NO. 14

15
IGLC2
SYSCQVTHEGSTVEK
SEQ ID NO. 15

16
ORM2
NWGLSFYADKPETTK
SEQ ID NO. 16

17
IGLC2
ATLVCLISDFYPGAVTVAWK
SEQ ID NO. 17

18
FBLN1
AITPPHPASQANIIFDITEGNLR
SEQ ID NO. 18

19
HBD
VNVDAVGGEALGR
SEQ ID NO. 19

20
STOM
EASMVITESPAALQLR
SEQ ID NO. 20

21
FGA
HPDEAAFFDTASTGK
SEQ ID NO. 21

22
AMBP
ETLLQDFR
SEQ ID NO. 22

23
AMBP
IHIGSSFEK
SEQ ID NO. 23

24
C3
SSLSVPYVIVPLK
SEQ ID NO. 24

25
C3
IPIEDGSGEVVLSR
SEQ ID NO. 25

26
GUSB
SLLEQYHLGLDQK
SEQ ID NO. 26

27
C9
LSPIYNLVPVK
SEQ ID NO. 27

28
CCT7
SLHDAIMIVR
SEQ ID NO. 28

29
PSG2
NSATGEESSTSLTVK
SEQ ID NO. 28

30
PSG2
SDPVTLNLLHGPDLPR
SEQ ID NO. 30

31
CFB
DFHINLFQVLPWLK
SEQ ID NO. 31

32
HBB
GTFATLSELHCDK
SEQ ID NO. 32

33
SERPINA1
LYHSEAFTVNFGDTEEAK
SEQ ID NO. 33

34
APOC3
GWVTDGFSSLK
SEQ ID NO. 34

35
FGG
IHLISTQSAIPYALR
SEQ ID NO. 35

36
IGHG2
NQVSLTCLVK
SEQ ID NO. 36

The top 9 peptides (Table 3) were then used in a Random forest analysis where 80% of the data (EOP n = 18, PC n = 14) was used for training and 20% of the data (EOP n = 4, PC n = 4) was used for validation resulted in ROC AUC = 1, Specificity = 100% and Sensitivity = 100%. The addition of further peptides did not improve classification.

TABLE 3

Random forest classification of the healthy pregnant

and EOP patients based on the 9 top peptides

Classified as:
Healthy pregnant
EOP

Healthy pregnant
4
0

EOP
0
4

EXAMPLE 4

Peptide Expression in UM EOP Patients

A peptide level analysis was carried out to assess the risk stratification for UM EOP further. 204 peptides from 63 proteins were quantified by mapDIA and were subjected to statistical analysis. Student's t-test analysis with an FDR of 5% and S0 of 0.1 (denoted by the black hyperbolic lines, (FIG. 4) was performed on the 54 peptides meeting the minimal difference criteria (>1/<−1). 30 peptides were found differentially expressed in UM EOP samples, with 14 peptides decreased (right; ESKPLTAQQTTK, IHIGSSFEK, IFSPNVVNLTLVDLPGMTK, GTFSQLSELHCDK, ALTPQCGSGEDLYILTGTVPSDYR, VIAAEGEMNASR, VQHIQLLQK, GLIDEVNQDFTNR, VVEESELAR, TDFLIFDPK, LFIPQITTK, DIPQPHAEPWAFSLDLGLK, LLSDFPVVPTATR, IEINFPAEYPFKPPK) and 16 peptides increased (left; GGSASTWLTAFALR, MVETTAYALLTSLNLK, FTFTLHLETPKPSISSSNLNPR, LPKPYITINNLNPR, SYSCQVTHEGSTVEK, EQLGEFYEALDCLCIPR, VYGALMWSLGK, SVSIGYLLVK, VQAAVGTSAAPVPSDNH, SNPVILNVLYGPDLPR, RPWNVASLIYETK, DVFLGMFLYEYAR, LVRPEVDVMCTAFHDNEETFLK, AATITATSPGALWGLDR, ITLPDFTGDLR, FPVEMTHNHNFR) in UM EOP compared to SM EOP.

FIG. 4 illustrates a volcano plot representing the peptides significantly altered in UM EOP patients. The black hyperbolic curved lines show the threshold for statistical significance, with an FDR of 0.05 and a minimal difference of 0.1. Our analysis revealed that the secretion levels of 16 peptides were significantly increased in UM EOP PR (circle) whereas 14 peptides were significantly decreased in UM EOP PR (triangle).

EXAMPLE 5

Peptide Separation of Patients into SM EOP or UM EOP

The hierarchical clustering and principal component analysis of the 30 differential peptides illustrate a complete separation of SM EOP and UM EOP patients (FIG. 5).

FIG. 5(A) illustrates euclidian distance based two-way hierarchical clustering of the log²transformed quantitative values of the 30 peptides (16 increased and 14 decreased) differentially released in UM EOP PR completely separates the samples into SM EOP and UM EOP clusters. FIG. 5(B) illustrates principal component analysis of log²transformed quantitative values of the 30 differential peptides. Combination of component 1 and 2 enables complete separation of SM EOP (triangle) and UM EOP (circle) PR samples.

The peptides deemed differentially expressed in SM and UM EOP samples were furthermore subjected to machine learning analysis with the WEKA software. The peptides were subjected to InfoGain attribute selection and ranked according to their ability to separate the groups (Table 4).

TABLE 4

Attribute selection ranking for the 30 differential UM EOP peptides

Rank
Gene name
Peptide
SEQID

1
FN1
ESKPLTAQQTTK
SEQ ID NO. 37

2
CPB2
IHIGSSFEK
SEQ ID NO. 7

3
C5
GGSASTWLTAFALR
SEQ ID NO. 10

4
C5
MVETTAYALLTSLNLK
SEQ ID NO. 38

5
PSG1
FTFTLHLETPKPSISSSNLNPR
SEQ ID NO. 11

6
DNM1L
IFSPNVVNLTLVDLPGMTK
SEQ ID NO. 12

7
PSG1
LPKPYITINNLNPR
SEQ ID NO. 39

8
IGLC2
SYSCQVTHEGSTVEK
SEQ ID NO. 15

9
HBD
GTFSQLSELHCDK
SEQ ID NO. 40

10
ORM2
EQLGEFYEALDCLCIPR
SEQ ID NO. 41

11
ENDOD1
ALTPQCGSGEDLYILTGTVPSDYR
SEQ ID NO. 8

12
EHD1
VYGALMWSLGK
SEQ ID NO. 9

13
STOM
VIAAEGEMNASR
SEQ ID NO. 42

14
FGA
VQHIQLLQK
SEQ ID NO. 43

15
COL4A2
SVSIGYLLVK
SEQ ID NO. 44

16
APOE
VQAAVGTSAAPVPSDNH
SEQ ID NO. 45

17
PSG9
SNPVILNVLYGPDLPR
SEQ ID NO. 46

18
FGA
GLIDEVNQDFTNR
SEQ ID NO. 47

19
C9
VVEESELAR
SEQ ID NO. 48

20
C9
RPWNVASLIYETK
SEQ ID NO. 49

21
ALB
DVFLGMFLYEYAR
SEQ ID NO. 50

22
ALB
LVRPEVDVMCTAFHDNEETFLK
SEQ ID NO. 51

23
PRKAR2B
AATITATSPGALWGLDR
SEQ ID NO. 52

24
LBP
ITLPDFTGDLR
SEQ ID NO. 53

25
HPSE
TDFLIFDPK
SEQ ID NO. 54

26
PSG3
LFIPQITTK
SEQ ID NO. 55

27
SHBG
DIPQPHAEPWAFSLDLGLK
SEQ ID NO. 56

28
SVEP1
LLSDFPVVPTATR
SEQ ID NO. 57

29
UBE2L3
IEINFPAEYPFKPPK
SEQ ID NO. 58

30
SERPIND1
FPVEMTHNHNFR
SEQ ID NO. 59

The top 10 peptides (Table 5) were then used in a Random forest analysis where 60% of the data (SM EOP n = 2, UM EOP n = 5) was used for training and 40% of the data (SM EOP n = 3, UM EOP n = 1) was used for validation resulted in ROC AUC = 1, Specificity = 100% and Sensitivity = 100% The addition of further peptides did not improve classification.

TABLE 5

Random forest classification of the SM EOP and

UM EOP patients based on the 11 top peptides

Classified as:
SM EOP
UM EOP

SM EOP
3
0

UM EOP
0
1

TABLE 6

Overlapping proteins and peptides that diagnose preeclampsia; and/or prognose unstable

moderate early-onset preeclampsia

Peptide in

Gene
Peptide in
prognostic

Name
diagnostic panel
panel
Protein
PE v Normal
UM EOP v SM EOP

ORM2
NWGLSFYADKPETTK
EQLGEFYEAL
Alpha-1-
a b ←classified as
a b ←classified as

(SEQ ID. 16)
DCLCIPR (SEQ
acid
31|a = PC
31|a = PC

ID. 41)
glycoprotein
04|b = PE
04|b = PE

2

IGLC2
SYSCQVTHEGSTVEK
SYSCQVTHEG
Immuno-
a b ←classified as
a b ←classified as

(SEQ ID. 15);
STVEK (SEQ
globulin
31|a = PC
31|a = PC

ATLVCLISDFYPGAVTV
ID. 15)
lambda
13|b = PE
04|b = PE

AWK (SEQ ID. 17)

constant 2

C5
GGSASTWLTAFALR
GGSASTWLTA
Complement
a b ←classified as
a b ←classified as

(SEQ ID. 10)
FALR (SEQ
C5
31|a = PC
31|a = PC

ID. 10);

04|b = PE
04|b = PE

MVETTAYALLT

SLNLK (SEQ

ID. 38)

C9
LSPIYNLVPVK (SEQ
VVEESELAR
Complement
a b ←classified as
a b ←classified as

ID. 27)
(SEQ ID. 48);
component
13|a = PC
31|a = PC

RPWNVASLIY
C9
13|b = PE
13|b = PE

ETK (SEQ

ID. 49)

CPB2
IHIGSSFEK
IHIGSSFEK
Carboxy-
a b ←classified as
a b ←classified as

(SEQ ID. 7)
(SEQ ID. 7)
peptidase B2
40|a = PC
31|a = PC

04|b = PE
04|b = PE

ENDO
ALTPQCGSGEDLYILTG
ALTPQCGSGE
Endo-
a b ←classified as
a b ←classified as

D1
TVPSDYR (SEQ ID. 8)
DLYILTGTVPS
nuclease
40|a = PC
31|a = PC

DYR (SEQ
domain-
04|b = PE
04|b = PE

ID. 8)
containing

1 protein

FGA
HPDEAAFFDTASTGK
VQHIQLLQK
Fibrinogen
a b ←classified as
a b ←classified as

(SEQ ID.21)
(SEQ ID. 43);
alpha chain
22|a = PC
13|a = PC

GLIDEVNQDFT

04|b = PE
13|b = PE

NR (SEQ

ID. 47)

FN1
FGFCPMAAHEEICTTN
ESKPLTAQQT
Fibronectin
a b ←classified as
a b ←classified as

EGVMYR (SEQ ID. 1);
TK (SEQ ID. 37)

40|a = PC
31|a = PC

YSFCTDHTVLVQTR

04|b = PE
04|b = PE

(SEQ ID. 2);

VDVIPVNLPGEHGQR

(SEQ ID. 3);

EATIPGHLNSYTIK

(SEQ ID. 4);

IYLYTLNDNAR (SEQ

ID. 5);

TYLGNALVCTCYGGSR

(SEQ ID. 6)

HBD
VNVDAVGGEALGR
GTFSQLSELH
Hemoglobin
a b ←classified as
a b ←classified as

(SEQ ID. 19)
CDK (SEQ
subunit
13|a = PC
21|a = PEMM

ID. 40)
delta
04|b = PE
01|b = PEMS

PSG1
FTFTLHLETPKPSISSS
FTFTLHLETPK
Pregnancy-
a b ←classified as
a b ←classified as

NLNPR (SEQ ID. 11)
PSISSSNLNPR
specific
31|a = PC
31|a = PC

(SEQ ID. 11);
beta-1-
22|b = PE
04|b = PE

LPKPYITINNLN
glycoprotein

PR (SEQ ID. 39)
1

STOM
EASMVITESPAALQLR
VIAAEGEMNA
Erythrocyte
a b ←classified as
a b ←classified as

(SEQ ID. 20)
SR (SEQ ID. 42)
band 7
22|a = PC
40|a = PC

integral
13|b = PE
04|b = PE

membrane

protein

EHD1
VYGALMWSLGK (SEQ
VYGALMWSLG
EH
a b ←classified as
a b ←classified as

ID. 9)
K (SEQ ID. 9)
domain-
40|a = PC
21|a = PEMM

containing
04|b = PE
10|b = PEMS

protein 1

DNM1L
IFSPNVVNLTLVDLPGM
IFSPNVVNLTL
Dynamin 1
a b ←classified as
a b ←classified as

TK (SEQ ID. 12)
VDLPGMTK
Like protein
31|a = PC
30|a = PEMM

(SEQ ID. 12)

04|b = PE
01|b = PEMS

Table 6 illustrates the proteins and peptides that diagnose preeclampsia, and/or prognose unstable moderate early-onset preeclampsia.

CPB2, ENDOD1 and FN1 diagnose preeclampsia with 100% specificity.

ORM2, IGLC2, C5, CBP2, FN1, HBD and PSG1 progonse UM EOP with 100% specificity.

EXAMPLE 6

Peptide Expression in Plasma Samples

To investigate if platelet-releasate-uncovered biomarkers can be found in plasma of EOP women, two top biomarkers were selected to confirm that these PR effectors can be found in plasma of early-onset preeclampsia (EOP) women. These biomarkers were quantified using an antibody-based test.

In brief, plasma from 22 early-onset preeclampsia patients and 18 healthy pregnant controls were diluted with 3% BSA in TBS and 100 μI (for CPB2 detection), or diluted with a diluent provided with the antibody and 50 μl (for C5 detection), was incubated in a 96-well plate coated with a corresponding primary antibody (Abcam plc) for 2h for C5 and 30 min for CPB2 according to the manufacturer's instructions to facilitate protein binding. Following binding of the protein to the antibody, excess plasma was removed and any unbound proteins washed off according to the manufacturer's instructions. Samples were then incubated with a complementary antibody (Abcam plc) for 1 h (C5) or 30 min (CPB2). For the detection of C5, excess of complementary antibody was washed off and samples were incubated further with conjugate antibody (Abcam plc) according to the manufacturer's instructions. The excess of the antibody was washed off again, and a chemiluminescent substrate was added to the samples for 10 min to allow for reaction to occur according to the manufacturer's instructions. The reaction was stopped, and the absorbance reading obtained at 450 nm. Protein concentration was calculated using standards of known concentrations.

The top biomarkers (C5 and CPB2) were found and quantified in the plasma of EOP patients (including unstable moderate and stable moderate EOP patients) and healthy pregnant controls (see FIG. 6.

Results

Pearson correlation coefficient (r) and coefficient of variation (CV) analysis was performed to assess biological variability in protein abundances. We found strong inter-donor reproducibility across our PR samples, averaging at r=0.932±0.034 for healthy pregnant controls (see FIGS. 1 (A&B)) and r =0.926±0.033 for EOP patients (including stable moderate (SM), unstable moderate (UM) and stable severe (SS) EOP patients) (see FIGS. 1 (A&B)). The average variance in protein quantitation was very low both in pregnant control (average CV=5.8%±2.5%) and EOP PR (average CV=5.9%±2.6%), indicating that PR contents were highly reproducible across donors. Moreover, maximum variance was low in both pregnant control and EOP PR (24.6% and 21.9% respectively; see FIG. 1 (C)). Collectively, these data suggest that the PR is a stable proteome in healthy pregnant and EOP women with vast majority of proteins exhibiting <20% associated variability.

A METHOD OF DIAGNOSING AND/OR PROGNOSING PREECLAMPSIA

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