WO2022171318A1 - In vitro method for determining the risk of suffering from preeclampsia - Google Patents
In vitro method for determining the risk of suffering from preeclampsia Download PDFInfo
- Publication number
- WO2022171318A1 WO2022171318A1 PCT/EP2021/079425 EP2021079425W WO2022171318A1 WO 2022171318 A1 WO2022171318 A1 WO 2022171318A1 EP 2021079425 W EP2021079425 W EP 2021079425W WO 2022171318 A1 WO2022171318 A1 WO 2022171318A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- expression
- gene
- preeclampsia
- genes
- suffering
- Prior art date
Links
- 201000011461 pre-eclampsia Diseases 0.000 title claims abstract description 162
- 238000000034 method Methods 0.000 title claims abstract description 153
- 238000000338 in vitro Methods 0.000 title claims abstract description 70
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 384
- 230000014509 gene expression Effects 0.000 claims abstract description 247
- 230000027758 ovulation cycle Effects 0.000 claims abstract description 49
- 239000012472 biological sample Substances 0.000 claims abstract description 43
- 230000008859 change Effects 0.000 claims description 62
- 239000000523 sample Substances 0.000 claims description 55
- 108090000468 progesterone receptors Proteins 0.000 claims description 54
- 230000002357 endometrial effect Effects 0.000 claims description 51
- 102000003998 progesterone receptors Human genes 0.000 claims description 51
- 238000004393 prognosis Methods 0.000 claims description 33
- 102100038595 Estrogen receptor Human genes 0.000 claims description 28
- 108010007005 Estrogen Receptor alpha Proteins 0.000 claims description 24
- 239000003153 chemical reaction reagent Substances 0.000 claims description 20
- 230000002829 reductive effect Effects 0.000 claims description 10
- 238000002560 therapeutic procedure Methods 0.000 claims description 7
- 238000005259 measurement Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 201000005608 severe pre-eclampsia Diseases 0.000 description 173
- 230000016117 decidualization Effects 0.000 description 84
- 238000004458 analytical method Methods 0.000 description 43
- 238000012163 sequencing technique Methods 0.000 description 34
- 238000003559 RNA-seq method Methods 0.000 description 33
- 230000035935 pregnancy Effects 0.000 description 32
- 210000001519 tissue Anatomy 0.000 description 30
- 238000013459 approach Methods 0.000 description 29
- 108091032973 (ribonucleotides)n+m Proteins 0.000 description 26
- 108020004414 DNA Proteins 0.000 description 26
- 238000000513 principal component analysis Methods 0.000 description 26
- 108020004635 Complementary DNA Proteins 0.000 description 25
- 238000010804 cDNA synthesis Methods 0.000 description 25
- 239000002299 complementary DNA Substances 0.000 description 24
- 238000001727 in vivo Methods 0.000 description 23
- 102000004169 proteins and genes Human genes 0.000 description 23
- 230000001105 regulatory effect Effects 0.000 description 23
- 238000003745 diagnosis Methods 0.000 description 22
- 238000011529 RT qPCR Methods 0.000 description 18
- 230000002950 deficient Effects 0.000 description 18
- 102000040430 polynucleotide Human genes 0.000 description 18
- 108091033319 polynucleotide Proteins 0.000 description 18
- 239000002157 polynucleotide Substances 0.000 description 18
- 108020004999 messenger RNA Proteins 0.000 description 16
- 238000005516 engineering process Methods 0.000 description 15
- 238000012360 testing method Methods 0.000 description 15
- 230000002103 transcriptional effect Effects 0.000 description 15
- 238000012549 training Methods 0.000 description 14
- 230000008774 maternal effect Effects 0.000 description 13
- 238000003762 quantitative reverse transcription PCR Methods 0.000 description 13
- 230000003248 secreting effect Effects 0.000 description 13
- RJKFOVLPORLFTN-LEKSSAKUSA-N Progesterone Chemical compound C1CC2=CC(=O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H](C(=O)C)[C@@]1(C)CC2 RJKFOVLPORLFTN-LEKSSAKUSA-N 0.000 description 12
- 150000007523 nucleic acids Chemical class 0.000 description 12
- CCEKAJIANROZEO-UHFFFAOYSA-N sulfluramid Chemical group CCNS(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F CCEKAJIANROZEO-UHFFFAOYSA-N 0.000 description 12
- 230000031018 biological processes and functions Effects 0.000 description 11
- 229940079593 drug Drugs 0.000 description 11
- 239000003814 drug Substances 0.000 description 11
- 239000012634 fragment Substances 0.000 description 11
- 102000039446 nucleic acids Human genes 0.000 description 11
- 108020004707 nucleic acids Proteins 0.000 description 11
- 239000002773 nucleotide Substances 0.000 description 11
- 125000003729 nucleotide group Chemical group 0.000 description 11
- 238000002360 preparation method Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 11
- 230000011664 signaling Effects 0.000 description 11
- -1 PR-A Proteins 0.000 description 10
- 238000001574 biopsy Methods 0.000 description 10
- 230000002074 deregulated effect Effects 0.000 description 10
- 201000010099 disease Diseases 0.000 description 10
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 10
- 108010090371 progesterone receptor B Proteins 0.000 description 10
- 108090000031 Hedgehog Proteins Proteins 0.000 description 9
- 102000003693 Hedgehog Proteins Human genes 0.000 description 9
- 230000036772 blood pressure Effects 0.000 description 9
- 238000002493 microarray Methods 0.000 description 9
- 238000007481 next generation sequencing Methods 0.000 description 9
- 239000012071 phase Substances 0.000 description 9
- 210000002536 stromal cell Anatomy 0.000 description 9
- 210000004696 endometrium Anatomy 0.000 description 8
- 238000002483 medication Methods 0.000 description 8
- 210000002993 trophoblast Anatomy 0.000 description 8
- 238000010200 validation analysis Methods 0.000 description 8
- 108010029485 Protein Isoforms Proteins 0.000 description 7
- 102000001708 Protein Isoforms Human genes 0.000 description 7
- 210000004027 cell Anatomy 0.000 description 7
- 230000007547 defect Effects 0.000 description 7
- 230000003831 deregulation Effects 0.000 description 7
- 238000011161 development Methods 0.000 description 7
- 230000018109 developmental process Effects 0.000 description 7
- 238000013399 early diagnosis Methods 0.000 description 7
- 238000009396 hybridization Methods 0.000 description 7
- 230000028993 immune response Effects 0.000 description 7
- 230000037361 pathway Effects 0.000 description 7
- 102000005962 receptors Human genes 0.000 description 7
- 108020003175 receptors Proteins 0.000 description 7
- 208000024891 symptom Diseases 0.000 description 7
- 108010057464 Prolactin Proteins 0.000 description 6
- 102000003946 Prolactin Human genes 0.000 description 6
- 239000000090 biomarker Substances 0.000 description 6
- 230000009087 cell motility Effects 0.000 description 6
- 230000000295 complement effect Effects 0.000 description 6
- 239000003246 corticosteroid Substances 0.000 description 6
- 238000001514 detection method Methods 0.000 description 6
- 238000009826 distribution Methods 0.000 description 6
- 230000003828 downregulation Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000009545 invasion Effects 0.000 description 6
- 238000010606 normalization Methods 0.000 description 6
- 239000000186 progesterone Substances 0.000 description 6
- 229960003387 progesterone Drugs 0.000 description 6
- 229940097325 prolactin Drugs 0.000 description 6
- 230000004044 response Effects 0.000 description 6
- 238000011282 treatment Methods 0.000 description 6
- 102000010834 Extracellular Matrix Proteins Human genes 0.000 description 5
- 108010037362 Extracellular Matrix Proteins Proteins 0.000 description 5
- 102000003971 Fibroblast Growth Factor 1 Human genes 0.000 description 5
- 108090000386 Fibroblast Growth Factor 1 Proteins 0.000 description 5
- 101000882584 Homo sapiens Estrogen receptor Proteins 0.000 description 5
- 229920001213 Polysorbate 20 Polymers 0.000 description 5
- 230000003321 amplification Effects 0.000 description 5
- 238000007405 data analysis Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 210000002744 extracellular matrix Anatomy 0.000 description 5
- 230000004547 gene signature Effects 0.000 description 5
- 229940088597 hormone Drugs 0.000 description 5
- 239000005556 hormone Substances 0.000 description 5
- 230000003993 interaction Effects 0.000 description 5
- 238000003199 nucleic acid amplification method Methods 0.000 description 5
- 239000000256 polyoxyethylene sorbitan monolaurate Substances 0.000 description 5
- 230000027272 reproductive process Effects 0.000 description 5
- 238000003196 serial analysis of gene expression Methods 0.000 description 5
- 101000990915 Homo sapiens Stromelysin-1 Proteins 0.000 description 4
- CSNNHWWHGAXBCP-UHFFFAOYSA-L Magnesium sulfate Chemical compound [Mg+2].[O-][S+2]([O-])([O-])[O-] CSNNHWWHGAXBCP-UHFFFAOYSA-L 0.000 description 4
- 108091034117 Oligonucleotide Proteins 0.000 description 4
- 102100030416 Stromelysin-1 Human genes 0.000 description 4
- 230000004075 alteration Effects 0.000 description 4
- 230000001773 anti-convulsant effect Effects 0.000 description 4
- 239000001961 anticonvulsive agent Substances 0.000 description 4
- 229960003965 antiepileptics Drugs 0.000 description 4
- 230000008901 benefit Effects 0.000 description 4
- 230000015556 catabolic process Effects 0.000 description 4
- 229960001334 corticosteroids Drugs 0.000 description 4
- 210000003785 decidua Anatomy 0.000 description 4
- 230000014818 extracellular matrix organization Effects 0.000 description 4
- 238000010230 functional analysis Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 239000012188 paraffin wax Substances 0.000 description 4
- 230000026731 phosphorylation Effects 0.000 description 4
- 238000006366 phosphorylation reaction Methods 0.000 description 4
- 102100036848 C-C motif chemokine 20 Human genes 0.000 description 3
- 102100036189 C-X-C motif chemokine 3 Human genes 0.000 description 3
- 102000053602 DNA Human genes 0.000 description 3
- 102100029110 Endothelin-2 Human genes 0.000 description 3
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 3
- 241000282412 Homo Species 0.000 description 3
- 101000713099 Homo sapiens C-C motif chemokine 20 Proteins 0.000 description 3
- 101000947193 Homo sapiens C-X-C motif chemokine 3 Proteins 0.000 description 3
- 101000841197 Homo sapiens Endothelin-2 Proteins 0.000 description 3
- 101000961156 Homo sapiens Immunoglobulin heavy constant gamma 1 Proteins 0.000 description 3
- 101001013150 Homo sapiens Interstitial collagenase Proteins 0.000 description 3
- 101000665841 Homo sapiens Receptor expression-enhancing protein 2 Proteins 0.000 description 3
- 102100039345 Immunoglobulin heavy constant gamma 1 Human genes 0.000 description 3
- 102000000380 Matrix Metalloproteinase 1 Human genes 0.000 description 3
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 description 3
- 229910019142 PO4 Inorganic materials 0.000 description 3
- 238000002123 RNA extraction Methods 0.000 description 3
- 102100038270 Receptor expression-enhancing protein 2 Human genes 0.000 description 3
- JLCPHMBAVCMARE-UHFFFAOYSA-N [3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[5-(2-amino-6-oxo-1H-purin-9-yl)-3-[[3-[[3-[[3-[[3-[[3-[[5-(2-amino-6-oxo-1H-purin-9-yl)-3-[[5-(2-amino-6-oxo-1H-purin-9-yl)-3-hydroxyoxolan-2-yl]methoxy-hydroxyphosphoryl]oxyoxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxyoxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methyl [5-(6-aminopurin-9-yl)-2-(hydroxymethyl)oxolan-3-yl] hydrogen phosphate Polymers Cc1cn(C2CC(OP(O)(=O)OCC3OC(CC3OP(O)(=O)OCC3OC(CC3O)n3cnc4c3nc(N)[nH]c4=O)n3cnc4c3nc(N)[nH]c4=O)C(COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3CO)n3cnc4c(N)ncnc34)n3ccc(N)nc3=O)n3cnc4c(N)ncnc34)n3ccc(N)nc3=O)n3ccc(N)nc3=O)n3ccc(N)nc3=O)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)n3cc(C)c(=O)[nH]c3=O)n3cc(C)c(=O)[nH]c3=O)n3ccc(N)nc3=O)n3cc(C)c(=O)[nH]c3=O)n3cnc4c3nc(N)[nH]c4=O)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)O2)c(=O)[nH]c1=O JLCPHMBAVCMARE-UHFFFAOYSA-N 0.000 description 3
- 230000033115 angiogenesis Effects 0.000 description 3
- 230000001580 bacterial effect Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 3
- 230000036755 cellular response Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000010195 expression analysis Methods 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 238000011223 gene expression profiling Methods 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 210000000987 immune system Anatomy 0.000 description 3
- 230000003834 intracellular effect Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 230000004060 metabolic process Effects 0.000 description 3
- 230000016087 ovulation Effects 0.000 description 3
- 239000010452 phosphate Substances 0.000 description 3
- 239000004417 polycarbonate Substances 0.000 description 3
- 230000004850 protein–protein interaction Effects 0.000 description 3
- 238000011002 quantification Methods 0.000 description 3
- 238000003753 real-time PCR Methods 0.000 description 3
- 230000028617 response to DNA damage stimulus Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000013515 script Methods 0.000 description 3
- 230000019491 signal transduction Effects 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 102000040650 (ribonucleotides)n+m Human genes 0.000 description 2
- FWBHETKCLVMNFS-UHFFFAOYSA-N 4',6-Diamino-2-phenylindol Chemical compound C1=CC(C(=N)N)=CC=C1C1=CC2=CC=C(C(N)=N)C=C2N1 FWBHETKCLVMNFS-UHFFFAOYSA-N 0.000 description 2
- 206010010904 Convulsion Diseases 0.000 description 2
- IVOMOUWHDPKRLL-KQYNXXCUSA-N Cyclic adenosine monophosphate Chemical compound C([C@H]1O2)OP(O)(=O)O[C@H]1[C@@H](O)[C@@H]2N1C(N=CN=C2N)=C2N=C1 IVOMOUWHDPKRLL-KQYNXXCUSA-N 0.000 description 2
- 239000003298 DNA probe Substances 0.000 description 2
- 238000001712 DNA sequencing Methods 0.000 description 2
- 108010014303 DNA-directed DNA polymerase Proteins 0.000 description 2
- 102000016928 DNA-directed DNA polymerase Human genes 0.000 description 2
- 101150099632 Dd gene Proteins 0.000 description 2
- 102100038604 Endoplasmic reticulum resident protein 27 Human genes 0.000 description 2
- 206010048554 Endothelial dysfunction Diseases 0.000 description 2
- 101150039965 Erp27 gene Proteins 0.000 description 2
- 201000005624 HELLP Syndrome Diseases 0.000 description 2
- 206010019670 Hepatic function abnormal Diseases 0.000 description 2
- 108020004996 Heterogeneous Nuclear RNA Proteins 0.000 description 2
- 102100040615 Homeobox protein MSX-2 Human genes 0.000 description 2
- 101000967222 Homo sapiens Homeobox protein MSX-2 Proteins 0.000 description 2
- 101000599852 Homo sapiens Intercellular adhesion molecule 1 Proteins 0.000 description 2
- 101000629400 Homo sapiens Mesoderm-specific transcript homolog protein Proteins 0.000 description 2
- 101000575378 Homo sapiens Microfibrillar-associated protein 2 Proteins 0.000 description 2
- 101001109689 Homo sapiens Nuclear receptor subfamily 4 group A member 3 Proteins 0.000 description 2
- 101000681218 Homo sapiens Transmembrane protein 215 Proteins 0.000 description 2
- 108090000144 Human Proteins Proteins 0.000 description 2
- 102000003839 Human Proteins Human genes 0.000 description 2
- 206010020772 Hypertension Diseases 0.000 description 2
- 102000004375 Insulin-like growth factor-binding protein 1 Human genes 0.000 description 2
- 108090000957 Insulin-like growth factor-binding protein 1 Proteins 0.000 description 2
- 102100034343 Integrase Human genes 0.000 description 2
- 102100037877 Intercellular adhesion molecule 1 Human genes 0.000 description 2
- 102100023539 Isthmin-1 Human genes 0.000 description 2
- 102100026821 Mesoderm-specific transcript homolog protein Human genes 0.000 description 2
- 102100025599 Microfibrillar-associated protein 2 Human genes 0.000 description 2
- 102100022673 Nuclear receptor subfamily 4 group A member 3 Human genes 0.000 description 2
- 108090000854 Oxidoreductases Proteins 0.000 description 2
- 102000004316 Oxidoreductases Human genes 0.000 description 2
- 238000012408 PCR amplification Methods 0.000 description 2
- 108010021757 Polynucleotide 5'-Hydroxyl-Kinase Proteins 0.000 description 2
- 102000008422 Polynucleotide 5'-hydroxyl-kinase Human genes 0.000 description 2
- 208000005107 Premature Birth Diseases 0.000 description 2
- 206010036590 Premature baby Diseases 0.000 description 2
- 206010037423 Pulmonary oedema Diseases 0.000 description 2
- 108020004518 RNA Probes Proteins 0.000 description 2
- 239000003391 RNA probe Substances 0.000 description 2
- 239000013614 RNA sample Substances 0.000 description 2
- 108010092799 RNA-directed DNA polymerase Proteins 0.000 description 2
- 208000001647 Renal Insufficiency Diseases 0.000 description 2
- 102100022300 Transmembrane protein 215 Human genes 0.000 description 2
- 206010047571 Visual impairment Diseases 0.000 description 2
- 102000013814 Wnt Human genes 0.000 description 2
- 108050003627 Wnt Proteins 0.000 description 2
- 230000021736 acetylation Effects 0.000 description 2
- 238000006640 acetylation reaction Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 230000010398 acute inflammatory response Effects 0.000 description 2
- 150000001298 alcohols Chemical class 0.000 description 2
- 150000001413 amino acids Chemical class 0.000 description 2
- 229940030600 antihypertensive agent Drugs 0.000 description 2
- 239000002220 antihypertensive agent Substances 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 238000003556 assay Methods 0.000 description 2
- 239000012620 biological material Substances 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
- 239000000872 buffer Substances 0.000 description 2
- 238000004113 cell culture Methods 0.000 description 2
- 230000022131 cell cycle Effects 0.000 description 2
- 230000024245 cell differentiation Effects 0.000 description 2
- 230000012292 cell migration Effects 0.000 description 2
- 230000004663 cell proliferation Effects 0.000 description 2
- 230000005754 cellular signaling Effects 0.000 description 2
- 230000002490 cerebral effect Effects 0.000 description 2
- 238000007385 chemical modification Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010367 cloning Methods 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 230000003205 diastolic effect Effects 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 230000008694 endothelial dysfunction Effects 0.000 description 2
- 238000013401 experimental design Methods 0.000 description 2
- 230000023410 extracellular matrix disassembly Effects 0.000 description 2
- 210000002950 fibroblast Anatomy 0.000 description 2
- 238000013467 fragmentation Methods 0.000 description 2
- 238000006062 fragmentation reaction Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 210000004907 gland Anatomy 0.000 description 2
- 230000019360 gland morphogenesis Effects 0.000 description 2
- 230000013595 glycosylation Effects 0.000 description 2
- 238000006206 glycosylation reaction Methods 0.000 description 2
- 239000003102 growth factor Substances 0.000 description 2
- 238000007417 hierarchical cluster analysis Methods 0.000 description 2
- 238000012165 high-throughput sequencing Methods 0.000 description 2
- 230000003054 hormonal effect Effects 0.000 description 2
- 108091008039 hormone receptors Proteins 0.000 description 2
- 230000001771 impaired effect Effects 0.000 description 2
- 208000015181 infectious disease Diseases 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 201000006370 kidney failure Diseases 0.000 description 2
- 210000004185 liver Anatomy 0.000 description 2
- 210000004072 lung Anatomy 0.000 description 2
- 229910052943 magnesium sulfate Inorganic materials 0.000 description 2
- 235000019341 magnesium sulphate Nutrition 0.000 description 2
- 210000001161 mammalian embryo Anatomy 0.000 description 2
- 230000001404 mediated effect Effects 0.000 description 2
- 230000005906 menstruation Effects 0.000 description 2
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 description 2
- 229910052753 mercury Inorganic materials 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000010369 molecular cloning Methods 0.000 description 2
- 230000008506 pathogenesis Effects 0.000 description 2
- 210000002826 placenta Anatomy 0.000 description 2
- 230000028742 placenta development Effects 0.000 description 2
- 229920001184 polypeptide Polymers 0.000 description 2
- 230000001323 posttranslational effect Effects 0.000 description 2
- 102000004196 processed proteins & peptides Human genes 0.000 description 2
- 108090000765 processed proteins & peptides Proteins 0.000 description 2
- 230000000750 progressive effect Effects 0.000 description 2
- 201000001474 proteinuria Diseases 0.000 description 2
- 208000005333 pulmonary edema Diseases 0.000 description 2
- 238000000746 purification Methods 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000013074 reference sample Substances 0.000 description 2
- 230000029865 regulation of blood pressure Effects 0.000 description 2
- 230000022983 regulation of cell cycle Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000007480 sanger sequencing Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 239000007790 solid phase Substances 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 230000009885 systemic effect Effects 0.000 description 2
- 206010043554 thrombocytopenia Diseases 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000009966 trimming Methods 0.000 description 2
- 230000003966 vascular damage Effects 0.000 description 2
- GUAHPAJOXVYFON-ZETCQYMHSA-N (8S)-8-amino-7-oxononanoic acid zwitterion Chemical compound C[C@H](N)C(=O)CCCCCC(O)=O GUAHPAJOXVYFON-ZETCQYMHSA-N 0.000 description 1
- BSFODEXXVBBYOC-UHFFFAOYSA-N 8-[4-(dimethylamino)butan-2-ylamino]quinolin-6-ol Chemical compound C1=CN=C2C(NC(CCN(C)C)C)=CC(O)=CC2=C1 BSFODEXXVBBYOC-UHFFFAOYSA-N 0.000 description 1
- 102100036826 Aldehyde oxidase Human genes 0.000 description 1
- 102100022524 Alpha-1-antichymotrypsin Human genes 0.000 description 1
- 108091032955 Bacterial small RNA Proteins 0.000 description 1
- 241000283690 Bos taurus Species 0.000 description 1
- 102100022508 Cadherin-24 Human genes 0.000 description 1
- 102100032539 Calpain-3 Human genes 0.000 description 1
- 241000282472 Canis lupus familiaris Species 0.000 description 1
- 241000283707 Capra Species 0.000 description 1
- 102100023443 Centromere protein H Human genes 0.000 description 1
- 102100037327 Chondrolectin Human genes 0.000 description 1
- KRKNYBCHXYNGOX-UHFFFAOYSA-K Citrate Chemical compound [O-]C(=O)CC(O)(CC([O-])=O)C([O-])=O KRKNYBCHXYNGOX-UHFFFAOYSA-K 0.000 description 1
- 108091026890 Coding region Proteins 0.000 description 1
- 108010037462 Cyclooxygenase 2 Proteins 0.000 description 1
- 102100037753 DEP domain-containing protein 1A Human genes 0.000 description 1
- 102000012410 DNA Ligases Human genes 0.000 description 1
- 108010061982 DNA Ligases Proteins 0.000 description 1
- 230000005778 DNA damage Effects 0.000 description 1
- 231100000277 DNA damage Toxicity 0.000 description 1
- 102100027830 DNA repair protein XRCC2 Human genes 0.000 description 1
- 102100036218 DNA replication complex GINS protein PSF2 Human genes 0.000 description 1
- 102100025450 DNA replication factor Cdt1 Human genes 0.000 description 1
- 102100030442 Derlin-3 Human genes 0.000 description 1
- 102100023471 E-selectin Human genes 0.000 description 1
- 102100024739 E3 ubiquitin-protein ligase UHRF1 Human genes 0.000 description 1
- 102100035087 Ectoderm-neural cortex protein 1 Human genes 0.000 description 1
- 241000283086 Equidae Species 0.000 description 1
- 108700039887 Essential Genes Proteins 0.000 description 1
- 108060002716 Exonuclease Proteins 0.000 description 1
- 241000282326 Felis catus Species 0.000 description 1
- 102100031510 Fibrillin-2 Human genes 0.000 description 1
- 102100035292 Fibroblast growth factor 14 Human genes 0.000 description 1
- 102100028071 Fibroblast growth factor 7 Human genes 0.000 description 1
- 102100020828 Four-jointed box protein 1 Human genes 0.000 description 1
- 108700028146 Genetic Enhancer Elements Proteins 0.000 description 1
- 206010070538 Gestational hypertension Diseases 0.000 description 1
- 108090000369 Glutamate Carboxypeptidase II Proteins 0.000 description 1
- 102100041003 Glutamate carboxypeptidase 2 Human genes 0.000 description 1
- 101000928314 Homo sapiens Aldehyde oxidase Proteins 0.000 description 1
- 101000678026 Homo sapiens Alpha-1-antichymotrypsin Proteins 0.000 description 1
- 101000899448 Homo sapiens Cadherin-24 Proteins 0.000 description 1
- 101000867715 Homo sapiens Calpain-3 Proteins 0.000 description 1
- 101000907934 Homo sapiens Centromere protein H Proteins 0.000 description 1
- 101000879734 Homo sapiens Chondrolectin Proteins 0.000 description 1
- 101000950642 Homo sapiens DEP domain-containing protein 1A Proteins 0.000 description 1
- 101000649306 Homo sapiens DNA repair protein XRCC2 Proteins 0.000 description 1
- 101000736065 Homo sapiens DNA replication complex GINS protein PSF2 Proteins 0.000 description 1
- 101000914265 Homo sapiens DNA replication factor Cdt1 Proteins 0.000 description 1
- 101000842622 Homo sapiens Derlin-3 Proteins 0.000 description 1
- 101000622123 Homo sapiens E-selectin Proteins 0.000 description 1
- 101000760417 Homo sapiens E3 ubiquitin-protein ligase UHRF1 Proteins 0.000 description 1
- 101000877456 Homo sapiens Ectoderm-neural cortex protein 1 Proteins 0.000 description 1
- 101000846890 Homo sapiens Fibrillin-2 Proteins 0.000 description 1
- 101000878181 Homo sapiens Fibroblast growth factor 14 Proteins 0.000 description 1
- 101001060261 Homo sapiens Fibroblast growth factor 7 Proteins 0.000 description 1
- 101000932133 Homo sapiens Four-jointed box protein 1 Proteins 0.000 description 1
- 101000609417 Homo sapiens Inter-alpha-trypsin inhibitor heavy chain H5 Proteins 0.000 description 1
- 101000977636 Homo sapiens Isthmin-1 Proteins 0.000 description 1
- 101000972489 Homo sapiens Laminin subunit alpha-1 Proteins 0.000 description 1
- 101000990902 Homo sapiens Matrix metalloproteinase-9 Proteins 0.000 description 1
- 101000592685 Homo sapiens Meiotic nuclear division protein 1 homolog Proteins 0.000 description 1
- 101000957259 Homo sapiens Mitotic spindle assembly checkpoint protein MAD2A Proteins 0.000 description 1
- 101000589632 Homo sapiens N-acetylaspartate synthetase Proteins 0.000 description 1
- 101000577541 Homo sapiens Neuronal regeneration-related protein Proteins 0.000 description 1
- 101000621220 Homo sapiens POC1 centriolar protein homolog A Proteins 0.000 description 1
- 101001095308 Homo sapiens Periostin Proteins 0.000 description 1
- 101000983850 Homo sapiens Phosphatidate phosphatase LPIN3 Proteins 0.000 description 1
- 101000692259 Homo sapiens Phosphoprotein associated with glycosphingolipid-enriched microdomains 1 Proteins 0.000 description 1
- 101001091365 Homo sapiens Plasma kallikrein Proteins 0.000 description 1
- 101001116123 Homo sapiens Podocalyxin-like protein 2 Proteins 0.000 description 1
- 101000605534 Homo sapiens Prostate-specific antigen Proteins 0.000 description 1
- 101000804792 Homo sapiens Protein Wnt-5a Proteins 0.000 description 1
- 101000736906 Homo sapiens Protein prune homolog 2 Proteins 0.000 description 1
- 101000926206 Homo sapiens Putative glutathione hydrolase 3 proenzyme Proteins 0.000 description 1
- 101000755643 Homo sapiens RIMS-binding protein 2 Proteins 0.000 description 1
- 101001074548 Homo sapiens Regulating synaptic membrane exocytosis protein 2 Proteins 0.000 description 1
- 101001096330 Homo sapiens Retinoid-binding protein 7 Proteins 0.000 description 1
- 101001103771 Homo sapiens Ribonuclease H2 subunit A Proteins 0.000 description 1
- 101000864743 Homo sapiens Secreted frizzled-related protein 1 Proteins 0.000 description 1
- 101001087372 Homo sapiens Securin Proteins 0.000 description 1
- 101000864800 Homo sapiens Serine/threonine-protein kinase Sgk1 Proteins 0.000 description 1
- 101000974731 Homo sapiens Small conductance calcium-activated potassium channel protein 1 Proteins 0.000 description 1
- 101000577877 Homo sapiens Stromelysin-3 Proteins 0.000 description 1
- 101000763869 Homo sapiens TIMELESS-interacting protein Proteins 0.000 description 1
- 101000904152 Homo sapiens Transcription factor E2F1 Proteins 0.000 description 1
- 101000837581 Homo sapiens Ubiquitin-conjugating enzyme E2 T Proteins 0.000 description 1
- 101000788732 Homo sapiens Zinc finger protein 367 Proteins 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 102100039454 Inter-alpha-trypsin inhibitor heavy chain H5 Human genes 0.000 description 1
- 102000004889 Interleukin-6 Human genes 0.000 description 1
- 108090001005 Interleukin-6 Proteins 0.000 description 1
- 102100022746 Laminin subunit alpha-1 Human genes 0.000 description 1
- 241000124008 Mammalia Species 0.000 description 1
- 102100030412 Matrix metalloproteinase-9 Human genes 0.000 description 1
- 102100033679 Meiotic nuclear division protein 1 homolog Human genes 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 102100038792 Mitotic spindle assembly checkpoint protein MAD2A Human genes 0.000 description 1
- LRHPLDYGYMQRHN-UHFFFAOYSA-N N-Butanol Chemical class CCCCO LRHPLDYGYMQRHN-UHFFFAOYSA-N 0.000 description 1
- 102100032380 N-acetylaspartate synthetase Human genes 0.000 description 1
- 206010028813 Nausea Diseases 0.000 description 1
- 102100028745 Neuronal regeneration-related protein Human genes 0.000 description 1
- 108091028043 Nucleic acid sequence Proteins 0.000 description 1
- CTQNGGLPUBDAKN-UHFFFAOYSA-N O-Xylene Chemical compound CC1=CC=CC=C1C CTQNGGLPUBDAKN-UHFFFAOYSA-N 0.000 description 1
- 206010030113 Oedema Diseases 0.000 description 1
- 241000283973 Oryctolagus cuniculus Species 0.000 description 1
- 102100022778 POC1 centriolar protein homolog A Human genes 0.000 description 1
- 208000002193 Pain Diseases 0.000 description 1
- 229930040373 Paraformaldehyde Natural products 0.000 description 1
- 241001494479 Pecora Species 0.000 description 1
- 102100037765 Periostin Human genes 0.000 description 1
- 102100025728 Phosphatidate phosphatase LPIN3 Human genes 0.000 description 1
- 102100026066 Phosphoprotein associated with glycosphingolipid-enriched microdomains 1 Human genes 0.000 description 1
- 206010035138 Placental insufficiency Diseases 0.000 description 1
- 102100034869 Plasma kallikrein Human genes 0.000 description 1
- 102100024588 Podocalyxin-like protein 2 Human genes 0.000 description 1
- 239000004698 Polyethylene Substances 0.000 description 1
- 239000004743 Polypropylene Substances 0.000 description 1
- 208000002787 Pregnancy Complications Diseases 0.000 description 1
- 241000288906 Primates Species 0.000 description 1
- 102300034319 Progesterone receptor isoform B Human genes 0.000 description 1
- 102100028772 Proline dehydrogenase 1, mitochondrial Human genes 0.000 description 1
- 102100038280 Prostaglandin G/H synthase 2 Human genes 0.000 description 1
- 102100036040 Protein prune homolog 2 Human genes 0.000 description 1
- 102100034060 Putative glutathione hydrolase 3 proenzyme Human genes 0.000 description 1
- 101150064691 Q gene Proteins 0.000 description 1
- 102100022371 RIMS-binding protein 2 Human genes 0.000 description 1
- 108091034057 RNA (poly(A)) Proteins 0.000 description 1
- 238000010802 RNA extraction kit Methods 0.000 description 1
- 102100036266 Regulating synaptic membrane exocytosis protein 2 Human genes 0.000 description 1
- 102100037879 Retinoid-binding protein 7 Human genes 0.000 description 1
- 102100039493 Ribonuclease H2 subunit A Human genes 0.000 description 1
- 241000283984 Rodentia Species 0.000 description 1
- 102100030058 Secreted frizzled-related protein 1 Human genes 0.000 description 1
- 102100033004 Securin Human genes 0.000 description 1
- 102100030070 Serine/threonine-protein kinase Sgk1 Human genes 0.000 description 1
- 102100022747 Small conductance calcium-activated potassium channel protein 1 Human genes 0.000 description 1
- 108010085012 Steroid Receptors Proteins 0.000 description 1
- 102100028847 Stromelysin-3 Human genes 0.000 description 1
- 238000000692 Student's t-test Methods 0.000 description 1
- 241000282887 Suidae Species 0.000 description 1
- 101000987219 Sus scrofa Pregnancy-associated glycoprotein 1 Proteins 0.000 description 1
- 102100026813 TIMELESS-interacting protein Human genes 0.000 description 1
- 108091036066 Three prime untranslated region Proteins 0.000 description 1
- 102000040945 Transcription factor Human genes 0.000 description 1
- 108091023040 Transcription factor Proteins 0.000 description 1
- 102100024026 Transcription factor E2F1 Human genes 0.000 description 1
- 102100028705 Ubiquitin-conjugating enzyme E2 T Human genes 0.000 description 1
- 208000032594 Vascular Remodeling Diseases 0.000 description 1
- 206010047700 Vomiting Diseases 0.000 description 1
- 238000001793 Wilcoxon signed-rank test Methods 0.000 description 1
- 102000043366 Wnt-5a Human genes 0.000 description 1
- 102100025438 Zinc finger protein 367 Human genes 0.000 description 1
- 230000001476 alcoholic effect Effects 0.000 description 1
- 210000003423 ankle Anatomy 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 239000000427 antigen Substances 0.000 description 1
- 108091007433 antigens Proteins 0.000 description 1
- 102000036639 antigens Human genes 0.000 description 1
- 239000007864 aqueous solution Substances 0.000 description 1
- 230000004872 arterial blood pressure Effects 0.000 description 1
- 210000002565 arteriole Anatomy 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 239000011324 bead Substances 0.000 description 1
- 239000013060 biological fluid Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 238000010805 cDNA synthesis kit Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000023402 cell communication Effects 0.000 description 1
- 239000013592 cell lysate Substances 0.000 description 1
- 230000023549 cell-cell signaling Effects 0.000 description 1
- 108091092356 cellular DNA Proteins 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001351 cycling effect Effects 0.000 description 1
- 210000004292 cytoskeleton Anatomy 0.000 description 1
- SUYVUBYJARFZHO-RRKCRQDMSA-N dATP Chemical compound C1=NC=2C(N)=NC=NC=2N1[C@H]1C[C@H](O)[C@@H](COP(O)(=O)OP(O)(=O)OP(O)(O)=O)O1 SUYVUBYJARFZHO-RRKCRQDMSA-N 0.000 description 1
- SUYVUBYJARFZHO-UHFFFAOYSA-N dATP Natural products C1=NC=2C(N)=NC=NC=2N1C1CC(O)C(COP(O)(=O)OP(O)(=O)OP(O)(O)=O)O1 SUYVUBYJARFZHO-UHFFFAOYSA-N 0.000 description 1
- 238000012350 deep sequencing Methods 0.000 description 1
- 238000011143 downstream manufacturing Methods 0.000 description 1
- 230000004064 dysfunction Effects 0.000 description 1
- 201000006549 dyspepsia Diseases 0.000 description 1
- 230000008482 dysregulation Effects 0.000 description 1
- 239000012636 effector Substances 0.000 description 1
- 239000012149 elution buffer Substances 0.000 description 1
- 210000002889 endothelial cell Anatomy 0.000 description 1
- 230000003511 endothelial effect Effects 0.000 description 1
- 210000003038 endothelium Anatomy 0.000 description 1
- 239000003623 enhancer Substances 0.000 description 1
- 238000010201 enrichment analysis Methods 0.000 description 1
- 210000000981 epithelium Anatomy 0.000 description 1
- 102000013165 exonuclease Human genes 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 210000003608 fece Anatomy 0.000 description 1
- 230000001605 fetal effect Effects 0.000 description 1
- 210000002683 foot Anatomy 0.000 description 1
- 230000030279 gene silencing Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 208000024798 heartburn Diseases 0.000 description 1
- 230000013632 homeostatic process Effects 0.000 description 1
- 238000002657 hormone replacement therapy Methods 0.000 description 1
- 238000001794 hormone therapy Methods 0.000 description 1
- 125000002887 hydroxy group Chemical group [H]O* 0.000 description 1
- 210000002865 immune cell Anatomy 0.000 description 1
- 238000010166 immunofluorescence Methods 0.000 description 1
- 238000002991 immunohistochemical analysis Methods 0.000 description 1
- 238000012744 immunostaining Methods 0.000 description 1
- 238000002513 implantation Methods 0.000 description 1
- 238000010874 in vitro model Methods 0.000 description 1
- 238000011534 incubation Methods 0.000 description 1
- 230000008798 inflammatory stress Effects 0.000 description 1
- 230000004941 influx Effects 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 210000000265 leukocyte Anatomy 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 210000004698 lymphocyte Anatomy 0.000 description 1
- 206010025482 malaise Diseases 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000012775 microarray technology Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000002991 molded plastic Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 230000016557 multi-organism process Effects 0.000 description 1
- 230000008693 nausea Effects 0.000 description 1
- 210000000653 nervous system Anatomy 0.000 description 1
- 108091027963 non-coding RNA Proteins 0.000 description 1
- 102000042567 non-coding RNA Human genes 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003960 organic solvent Substances 0.000 description 1
- 230000036542 oxidative stress Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 229920002866 paraformaldehyde Polymers 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 230000007310 pathophysiology Effects 0.000 description 1
- 230000010412 perfusion Effects 0.000 description 1
- 210000005259 peripheral blood Anatomy 0.000 description 1
- 239000011886 peripheral blood Substances 0.000 description 1
- 238000001558 permutation test Methods 0.000 description 1
- NBIIXXVUZAFLBC-UHFFFAOYSA-K phosphate Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 description 1
- 125000002467 phosphate group Chemical group [H]OP(=O)(O[H])O[*] 0.000 description 1
- 230000000865 phosphorylative effect Effects 0.000 description 1
- 230000004962 physiological condition Effects 0.000 description 1
- 210000002381 plasma Anatomy 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 229920003023 plastic Polymers 0.000 description 1
- 239000002798 polar solvent Substances 0.000 description 1
- 229920000515 polycarbonate Polymers 0.000 description 1
- 229920000573 polyethylene Polymers 0.000 description 1
- 235000010486 polyoxyethylene sorbitan monolaurate Nutrition 0.000 description 1
- 229920001155 polypropylene Polymers 0.000 description 1
- 238000011176 pooling Methods 0.000 description 1
- 230000001124 posttranscriptional effect Effects 0.000 description 1
- 239000003761 preservation solution Substances 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000037452 priming Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000035755 proliferation Effects 0.000 description 1
- 108020004930 proline dehydrogenase Proteins 0.000 description 1
- BDERNNFJNOPAEC-UHFFFAOYSA-N propan-1-ol Chemical class CCCO BDERNNFJNOPAEC-UHFFFAOYSA-N 0.000 description 1
- 230000000069 prophylactic effect Effects 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 230000009257 reactivity Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000007634 remodeling Methods 0.000 description 1
- 230000008672 reprogramming Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000008268 response to external stimulus Effects 0.000 description 1
- 230000021670 response to stimulus Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000003161 ribonuclease inhibitor Substances 0.000 description 1
- 210000003296 saliva Anatomy 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 210000002966 serum Anatomy 0.000 description 1
- 208000018316 severe headache Diseases 0.000 description 1
- 238000012174 single-cell RNA sequencing Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 102000005969 steroid hormone receptors Human genes 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
- 208000011580 syndromic disease Diseases 0.000 description 1
- 238000010937 topological data analysis Methods 0.000 description 1
- 238000013518 transcription Methods 0.000 description 1
- 230000035897 transcription Effects 0.000 description 1
- 238000012085 transcriptional profiling Methods 0.000 description 1
- 238000011222 transcriptome analysis Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 210000002700 urine Anatomy 0.000 description 1
- 108700026220 vif Genes Proteins 0.000 description 1
- 230000008673 vomiting Effects 0.000 description 1
- 239000011534 wash buffer Substances 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
- 230000004584 weight gain Effects 0.000 description 1
- 235000019786 weight gain Nutrition 0.000 description 1
- 239000008096 xylene Substances 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
Definitions
- the present invention refers to the medical field. Particularly, it refers to an in vitro method for the prognosis of preeclampsia as well as for determining the risk of suffering from preeclampsia.
- the method of the invention is particularly characterized in that it is based on determining the level of expression of genes measured in an endometrial tissue sample obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.
- PE Preeclampsia
- sPE severe preeclampsia
- sPE severe preeclampsia
- women suffers a further elevation of blood pressure (systolic >160 mm Hg or diastolic of >100 mm Hg) or any of the following: thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema and the new onset of cerebral or visual disturbances.
- the placenta plays a central role in PE pathophysiology with deficient cytotrophoblast (CTB) invasion of uterine decidua and spiral arterioles producing an incomplete endovascular invasion and altered uteroplacental perfusion.
- CTB cytotrophoblast
- the unsolved question is why shallow CTB invasion occurs.
- Pregnancy health is determined not only by the embryo -and the placenta- but also by the quality of the maternal decidua, where CTB invasion and remodeling of the maternal spiral arteries occurs.
- the contribution of the decidua to the etiology of PE, sPE, and placenta accrete has been suggested.
- Decidualization is the remodelling of the maternal endometrium initiated after ovulation necessary for adequate trophoblast invasion and subsequent placentation.
- the formation of the decidua in humans is a conceptus-independent process driven by the progesterone secreted after ovulation and local cyclic Adenosine Monophosphate (cAMP) that through progesterone receptor activation stimulates synthesis of a complex network of intracellular and secreted proteins.
- Endometrial decidualization involves secretory transformation of the uterine glands, influx of specialized immune cells, vascular remodeling, and the morphological, biochemical and transcriptional reprogramming of the endometrial stromal compartment.
- Morphologically is characterized by the transformation of elongated fibroblast-like endometrial stromal cells (ESCs) into enlarged polygonal/round cells shaped by a complex intracellular cytoskeleton rearrangement.
- Decidualized ESCs secrete biomarkers such as prolactin (PRL) and insulin-like growth factor binding protein- 1 (IGFBP1).
- PRL prolactin
- IGFBP1 insulin-like growth factor binding protein- 1
- Defective decidualization entails the inability of the endometrial compartment to undertake tissue differentiation leading to aberrations in trophoblast invasion and placentation, compromising pregnancy health like in sPE. Most of our knowledge about this function in health and disease has been generated from in vitro model systems.
- the present invention is focused on solving this problem and a specific preconceptional endometrial transcriptomic signature is herein presented, which is associated with defective decidualization (DD) that might contribute to sPE.
- DD defective decidualization
- the present invention refers to an in vitro method for the prognosis of preeclampsia.
- the present invention also refers to an in vitro method for determining the risk of suffering from preeclampsia in a subject.
- the present invention also relates to an in vitro method for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, wherein early diagnosis is preconception diagnosis, i.e. diagnosis that is carried out before conception.
- diagnosis is preferably early diagnosis
- preconception diagnosis i.e. diagnosis that is carried out before conception.
- the first embodiment of the present invention refers to an in vitro method for the prognosis of preeclampsia which comprises: a) Measuring the expression level of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle; and b) wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre- established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
- the invention refers to a method as defined above wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
- diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
- any gene included in Table 3 could be used for the prognosis of preeclampsia as far as the expression of the gene is measured in a sample, preferably endometrial tissue, obtained from the patient 1 to 6 days before the end of the menstrual cycle.
- a sample preferably endometrial tissue
- the samples should be collected during late secretory menstrual cycle (1 to 6 before menstrual cycle ends) to detect transcriptional differences between control and preeclampsia that let successful classify the samples into the two groups.
- the method of the invention comprises determining that the patient has a bad prognosis based on a log2fold change of at least 1 for the expression of at least a gene included in Table 3, a log2fold change of at least 2 for the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 for the expression of at least a gene included in Table 9 or a log2 fold change of at least 3 for the expression of at least a gene included in Table 10.
- the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
- the method of the invention comprises: a) Measuring the expression level of all genes comprised in Table 10, or in Table 9, or in Table 8, or in Table 3, or in Table 4, or in Table 3, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b) wherein a deviation in the level of expression of the genes measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
- the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
- the method of the invention comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2fold change of at least 1 for the expression of all genes included in Table 3, a log2fold change of at least 2 for the expression all genes included in Table 8, a log2 fold change of at least 2.5 for the expression all genes included in Table 9 or a log2 fold change of at least 3 for the expression of all genes included in Table 10.
- the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
- the method of the invention comprises: a) Measuring the expression level of progesterone receptor in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b) wherein the determination of a lower the level of expression in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
- the method of the invention comprises: a) Entering into the computer the level of expression of the genes obtained from healthy control subjects; b) entering into the computer the level of expression of the genes obtained in the step a) of the previous claims; c) producing a score which is displayed on the device; and d) determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with a pre-established threshold level of expression determined in healthy control subjects.
- the biological sample is an endometrial tissue sample.
- the second embodiment of the present invention refers to the use in vitro use of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, for the prognosis of preeclampsia, in a biological sample, preferably endometrial tissue, which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
- the invention refers to the use as defined in this paragraph wherein the use os aimed at the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
- the third embodiment of the present invention refers to a kit for implementing the method of the invention which comprises: a) Reagents for measuring the level of expression of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, and b) tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle.
- the fourth embodiment of the present invention refers to the use of the above defined kit for the prognosis of preeclampsia.
- the fifth embodiment of the present invention refers to an in vitro method for obtaining information from patients who may be suffering from preeclampsia which comprises: a) Measuring the expression level of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.
- the fifth embodiment of the present invention refers to a method for treating preeclampsia which comprises the administration of a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once the patient has been diagnosed by following the above explained method of the invention.
- Treatments which could be used for treating preeclampsia include:
- Corticosteroids If you have severe preeclampsia or HELLP syndrome, corticosteroid medications can temporarily improve liver and platelet function to help prolong your pregnancy. Corticosteroids can also help your baby's lungs become more mature in as little as 48 hours, an important step in preparing a premature baby for life outside the womb.
- Anticonvulsant medications If your preeclampsia is severe, your doctor may prescribe an anticonvulsant medication, such as magnesium sulphate, to prevent a first seizure.
- an anticonvulsant medication such as magnesium sulphate
- a further aspect of the invention includes an in vitro method for determining the risk of suffering from preeclampsia in a subject, which comprises:
- the invention relates to an in vitro method for determining the risk of suffering from preeclampsia, which comprises:
- step (ii) Wherein reduced expression levels of expression of the progesterone receptor or of the estrogen receptor 1 in step (i) as compared with a pre-established threshold level of expression, is an indication that the patient is at risk of suffering from preeclampsia.
- step (ii) Monitoring the subjects which have been determined in step (i) to be at high risk of suffering preeclampsia in order to detect the appearance of preeclampsia and
- the invention relates to an in vitro use of the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, Table 3 or Table 12 for determining the risk of suffering from preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
- the invention relates to a kit for implementing any of the methods according to the invention, which comprises:
- the invention relates to the use of the kit according to the invention for determining the risk of suffering from preeclampsia.
- FIG. 1 Global transcriptomics RNAseq results revealed 859 DEGs in sPE.
- A Schematic drawing of the study design used to identify and validate the DD-fmgerprinting in sPE.
- B The statistical significance versus gene expression fold change is displayed as a volcano plot drawing the global RNA-seq results. Threshold indicates: pval-FC (FDR ⁇ 0.05 and log2-fold- change>l); pval (green dots; FDR ⁇ 0.05 and log2-fold-change>l); FC (blue dots; FDR>0.05 and log2-fold-change>l) and none (purple dots; FDR>0.05 and log2-fold-change>l).
- FIG. 1 In vitro vs in vivo decidualization comparison.
- A Venn diagram displaying the number of common genes between previous vitro (left) and current in vivo approach (right). Eighteen genes are overlapping in both approaches.
- ⁇ B Box plot showing the average expression of the 18 common genes between control (blue boxes) and sPE (orange boxes). ( The fold change between control and sPE was assessed by RT-qPCR (grey bars) and by sequencing (green bars) for 5 transcripts from the 18 transcripts in common in both approaches.
- FIG. 3 sPE-DD fingerprinting composed by 166 DEGs that are involved in remarkable biologic process.
- A Volcano plot showing the downregulated (blue) and upregulated (red) genes in sPE from the DD-fmgerprinting. Each point represents each gene. Grey points are the not selected genes obtained in the global RNAseq.
- B The five most highly downregulated biological process for each major category of process ⁇ red, Immune response; yellow, signaling; green, Extracellular matrix;Z>/we, Cell motility; grey , Blood pressure regulation; purple, Reproductive process) (Q Clustering of DD-fmgerprinting genes within terms is shown for: acute inflammatory response, extracellular matrix disassembly, regulation of systemic blood pressure, response to hormone.
- FIG. 4 Validation of the DD-fmgerprinting in sPE.
- PCA Principal component analysis
- Q Principal component analysis (PCA) based on the fingerprinting in the test set. Each sample is represented in the Figure as a colored point ⁇ blue, Control; orange, sPE).
- Progesterone receptor is highly connected with DD-fmgerprinting in sPE.
- ⁇ A Venn diagram displaying the number of genes included in the fingerprinting which are predominantly endometrium expressed and associated with PR, and its overlapping. Genes associated with PR refers to genes with a PR binding site or its expression change if PGR is silenced.
- ⁇ B Over representation analysis of biological process for the subset of fingerprinting genes which are predominantly expressed in endometrium and associated with PGR (performed using ConsensuspathDB).
- Q Gene expression levels of PR, PR-A, and PR-B were assessed for control vs. sPE by RT-qPCR (grey bars, Control; green bars, sPE). RT-qPCR values are expressed Mean ⁇ SE. *** P ⁇ 0.001, ** P ⁇ 0.01.
- D Progesterone-decidualization network in control pregnancy including the interaction of immune response and endothelium.
- E Progesterone-decidualization network in sPE pregnancy.
- FIG. Transcriptomic analysis based on gestational age at delivery of control samples.
- A PCA based on 18476 genes kept after filtering out lowly expressed genes.
- B Volcano plot. Volcano plot threshold at the legend indicates: none (do not have DE genes neither high fold- change); fc (high fold-change, but not DE genes). Plot based on 728 genes labeled as fc (blue) and 17748 genes labeled as none (purple).
- Figure 7 Validation of RNA-seq results with nine relevant genes.
- ⁇ A Boxplot showing the expression patterns of the nine genes selected obtained in RNA-seq for control (blue boxes) and sPE (orange boxes) group.
- B RT-qPCR (grey bars) validating the sequencing results (green bars). RT-qPCR values are expressed Mean ⁇ SE. *** P ⁇ 0.001.
- Dot colour represents the endometrial receptivity analysis (ERA) diagnostic for all samples (control and sPE).
- Figure 9 PCA based on normalized gene expression. Dot colour represents control (purple) or preeclamptic (yellow) samples.
- FIG. 10 Global RNA-seq transcriptomic results revealed 593 differentially expressed genes (DEGs) in severe preeclampsia (sPE) vs. control samples.
- DEGs differentially expressed genes
- sPE severe preeclampsia
- FC Statistical significance
- FIG. 11 Defective decidualization (DD) transcriptomics in vitro vs. in vivo.
- A Common genes between previous in vitro (left) and current in vivo approaches analyzing decidualization (right). Nine genes overlap in both approaches.
- B Box plot showing the average expression of the nine common genes between control (blue boxes) and severe preeclampsia (sPE) (orange boxes) samples.
- C From the 593 differentially expressed genes (DEGs) obtained by global RNA-seq, a subset of 263 DEGs were identified as genes with a human endometrial stromal cell (hESC) origin using the scRNA-seq data published by Wang et al., 2020.
- DEGs differentially expressed genes
- FIG. 12 Severe preeclampsia defective decidualization (sPE-DD) fingerprint composed of 120 differentially expressed genes (DEGs).
- DEGs differentially expressed genes
- A Volcano plot showing downregulated (blue) and upregulated (red) genes in sPE from the DD fingerprint. Each point represents one gene; gray points are the rest of the genes obtained in the global RNA-seq analysis.
- B The three most highly downregulated biological process for each major category (red, cell cycle; yellow, DNA damage response; green, cell signaling; blue, cellular response; gray, cell motility; purple, extracellular matrix; pink, immune response; brown, reproductive process). Enrichment index was calculated by -log(p-value).
- C Clustering of DD fingerprint genes shown for reproductive process, response to bacterial molecules, extracellular matrix organization, regulation of receptor signaling, and response to hormones.
- Figure 13 Validation of the defective decidualization (DD) fingerprint in severe preeclampsia (sPE).
- PCA Principal component analysis
- C PCA based on the fingerprinting in the test set. Each sample is represented as a colored point (blue, control; orange, sPE).
- Estrogen receptor 1 ER1
- progesterone receptor-B PR-B
- DD defective decidualization
- A Venn diagram displaying genes included in the fingerprinting (120) predominantly expressed in the endometrium based on Human Protein Atlas data that overlap with genes modulated by ESR1 described by Okur et al., 2016 (58) and genes associated with PGR silencing described by Mazur et al., 2015 (35).
- B Network showing the connections between proteins codified by DD fingerprinting and the hormonal receptors, ER1 and PR. Shapes indicate different clusters established by String k-means method.
- the present invention relates to an in vitro method for determining the risk of suffering from preeclampsia.
- the authors of the invention have found that the risk of suffering from preeclampsia in a patient can be determined by measuring the level of expression of at least one gene in a biological sample of a patient obtained from the patient 1 to 6 days before the end of the menstrual cycle, and comparing this expression level with a reference expression level.
- the method of the invention allows the identification of women at risk of developing preeclampsia, This may be used to the application of preventive prophylactic measures, medical supervision, medication and treatments before and during pregnancy to reduce maternal and fetal morbidity and mortality.
- the invention relates to an in vitro method for determining the risk of suffering from preeclampsia, which comprises:
- a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression is an indication that the patient is at risk of suffering from preeclampsia.
- risk of suffering from a disease refers to a likelihood or probability that a subject develops a clinical condition within a defined time interval.
- determining the risk of suffering from a disease is understood as the assessment of the future clinical status of a patient prior to any sign of disease or symptom in said patient. If a risk of suffering a disease is determined, the clinical status of the patient is likely to change into a status of clinical symptomatology within a given time period after the assessment of the future status of said patient. If the patient is determined not to be at risk of suffering a disease, the clinical status of the patient is likely not to change into a status of clinical symptomatology within a given time period after the assessment of the future status of said patient is determined.
- the term “suffering from preeclampsia” refers to the appearance of any symptoms or clinical condition related to the placental insufficiency syndrome. Suitable criteria for determining whether a subject suffers from preeclampsia can be found, for instance, in the clinical manual such as Poon LC et al. (The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention. Int J Gynaecol Obstet. 2019 May;145 Suppl l(Suppl 1): 1-33. doi: 10.1002/ijgo.12802. Erratum in: Int J Gynaecol Obstet.
- sensing from preeclampsia may also refer to any accompanying clinical signs of preeclampsia including but not limited to hypertension, proteinuria, maternal vascular damage, elevated blood pressure (systolic >160 or diastolic of >100 mm Hg) or thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema, cerebral or visual disturbances, and combinations thereof.
- preeclampsia may also refer to any accompanying clinical signs of preeclampsia including but not limited to hypertension, proteinuria, maternal vascular damage, elevated blood pressure (systolic >160 or diastolic of >100 mm Hg) or thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema, cerebral or visual disturbances, and combinations thereof.
- the patient is at risk of suffering from preeclampsia with a given sensitivity and specificity if the levels of expression of the at least one gene according to the invention in the sample of the patient deviates from a pre-established threshold level of expression.
- determining of the risk of suffering from preeclampsia” in a patient is based on the measurement of the level of expression of at least one gene according to the invention in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle. Accordingly, the method of the invention allows the determination of the risk of suffering preeclampsia before the start of pregnancy.
- the method comprises measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, in Table 3 or in Table 12 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
- expression level refers to the measurable quantity of gene product produced by the gene in a sample of the subject, wherein the gene product can be a transcriptional product or a translational product.
- the gene expression level can be quantified by measuring the messenger RNA levels of said gene or of the protein encoded by said gene.
- the expression level of the genes used in the method according to the invention can be determined by measuring the levels of mRNA encoded by said gene, or by measuring the levels of the protein encoded by said gene, i.e. the protein or variants thereof.
- Variants of the proteins encoded by the genes which are measured according to the method of the invention include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained.
- sample refers to biological material isolated from a subject.
- the biological sample contains any biological material suitable for detecting DNA, RNA or protein levels.
- the sample comprises genetic material, e.g., DNA, genomic DNA (gDNA), complementary DNA (cDNA), RNA, heterogeneous nuclear RNA (hnRNA), mRNA, etc., from the subject under study.
- the sample can be isolated from any suitable tissue or biological fluid such as, for example blood, saliva, plasma, serum, urine, cerebrospinal liquid (CSF), feces, a surgical specimen, a specimen obtained from a biopsy, and a tissue sample embedded in paraffin.
- the sample from the subject according to the methods of the present invention is an endometrial tissue sample.
- Gene expression levels can be quantified by measuring the messenger RNA levels of the gene or of the protein encoded by said gene or of the protein encoded by said gene or of variants thereof.
- Protein variants include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained.
- Said term encompasses the protein of any mammal species, including but not being limited to domestic and farm animals (cows, horses, pigs, sheep, goats, dogs, cats or rodents), primates and humans.
- the protein is a human protein.
- the biological sample may be treated to physically, mechanically or chemically disrupt tissue or cell structure, to release intracellular components into an aqueous or organic solution to prepare nucleic acids for further analysis.
- the nucleic acids are extracted from the sample by procedures known to the skilled person and commercially available.
- RNA is then extracted from frozen or fresh samples by any of the methods typical in the art, for example, Sambrook, J., et ah, 2001. Molecular cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, N. Y., Vol. 1-3.
- the RNA is extracted from formalin-fixed, paraffin embedded tissues.
- An exemplary deparaffmization method involves washing the paraffmized sample with an organic solvent, such as xylene, for example.
- Deparaffmized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example include, methanol, ethanol, propanols, and butanols.
- Deparaffmized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example.
- the sample is simultaneously deparaffmised and rehydrated.
- the sample is then lysed and RNA is extracted from the sample.
- kits may be used for RNA extraction from paraffin samples, such as PureLinkTM FFPE Total RNA Isolation Kit (Thermofisher Scientific Inc., US).
- RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (1987) Lab Invest. 56:A67, and De Andres et ak, BioTechniques 18:42044 (1995). Preferably, care is taken to avoid degradation of the RNA during the extraction process.
- Hybridization-based approaches typically involve incubating fluorescently labelled cDNA with custom-made microarrays or commercial high-density oligo microarrays. Specialized microarrays have also been designed; for example, arrays with probes spanning exon junctions can be used to detect and quantify distinct spliced isoforms. Genomic tiling microarrays that represent the genome at high density have been constructed and allow the mapping of transcribed regions to a very high resolution, from several base pairs to -100 bp.
- Hybridization-based approaches are high throughput and relatively inexpensive, except for high-resolution tiling arrays that interrogate large genomes.
- these methods have several limitations, which include: reliance upon existing knowledge about genome sequence; high background levels owing to cross-hybridization; and a limited dynamic range of detection owing to both background and saturation of signals.
- comparing expression levels across different experiments is often difficult and can require complicated normalization methods.
- sequence-based approaches directly determine the cDNA sequence.
- Sanger sequencing of cDNA or EST libraries was used, but this approach is relatively low throughput, expensive and generally not quantitative.
- Tag-based methods were developed to overcome these limitations, including serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequencing (MPSS). These tag-based sequencing approaches are high throughput and can provide precise, digital gene expression levels.
- SAGE serial analysis of gene expression
- CAGE cap analysis of gene expression
- MPSS massively parallel signature sequencing
- the present methods can also involve a larger-scale analysis of mRNA levels, e.g., the detection of a plurality of biomarkers (e.g., 2-10, or 5-50, or 10-100, or 50-500 or more at one time).
- the methods described here can also involve the step of conducting a transcriptomic analysis (i.e., the analysis of the complete set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition). Understanding the transcriptome can be important for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and disease and how the biomarkers disclosed herein are indicative or predictive of a particular condition (e.g., LM or LMS).
- LM or LMS a particular condition
- transcriptomics The key aims of transcriptomics are: to catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs; to determine the transcriptional structure of genes, in terms of their start sites, 5' and 3' ends, splicing patterns and other post-transcriptional modifications; and to quantify the changing expression levels of each transcript during development and under different conditions.
- RNA- Seq RNA sequencing
- the expression level of the gene or genes used in the first method of the invention are determined by RNAseq.
- RNAseq or"RNA-seq” is used to refer to a transcriptomic approach where the total complement of RNAs from a given sample is isolated and sequenced using high- throughput next generation sequencing (NGS) technologies (e.g., SOLiD, 454, Illumina, or ION Torrent).
- NGS next generation sequencing
- RNA-Seq uses deep-sequencing technologies.
- a population of RNA (total or fractionated, such as poly(A)+) is converted to a library of cDNA fragments with adaptors attached to one or both ends.
- Each molecule, with or without amplification, is then sequenced in a high-throughput manner to obtain short sequences from one end (single-end sequencing) or both ends (pair-end sequencing).
- the reads are typically 30-400 bp, depending on the DNA- sequencing technology used.
- any high-throughput sequencing technology can be used for RNA-Seq, e.g., the Illumina IG18, Applied Biosystems SOLiD22 and Roche 454 Life Science systems have already been applied for this purpose.
- the Helicos Biosciences tSMS system is also appropriate and has the added advantage of avoiding amplification of target cDNA.
- the resulting reads are either aligned to a reference genome or reference transcripts, or assembled de novo without the genomic sequence to produce a genome-scale transcription map that consists of both the transcriptional structure and/or level of expression for each gene.
- RNA-seq Transcriptome analysis by next-generation sequencing (RNA-seq) allows investigation of a transcriptome at unsurpassed resolution.
- RNA-seq is independent of a priori knowledge on the sequence under investigation.
- the transcriptome can be profiled by high throughput techniques including SAGE, microarray, and sequencing of clones from cDNA libraries.
- SAGE SAGE
- microarray sequencing of clones from cDNA libraries.
- microarray technology suffers from well- known limitations including insufficient sensitivity for quantifying lower abundant transcripts, narrow dynamic range and biases arising from non-specific hybridizations. Additionally, microarrays are limited to only measuring known/annotated transcripts and often suffer from inaccurate annotations.
- Sequencing -based methods such as SAGE rely upon cloning and sequencing cDNA fragments. This approach allows quantification of mRNA abundance by counting the number of times cDNA fragments from a corresponding transcript are represented in a given sample, assuming that cDNA fragments sequenced contain sufficient information to identify a transcript. Sequencing-based approaches have a number of significant technical advantages over hybridization- based microarray methods. The output from sequence-based protocols is digital, rather than analog, obviating the need for complex algorithms for data normalization and summarization while allowing for more precise quantification and greater ease of comparison between results obtained from different samples. Consequently, the dynamic range is essentially infinite, if one accumulates enough sequence tags.
- next-generation sequencing (NGS) technology eliminates some of these barriers, enabling massive parallel sequencing at a high but reasonable cost for small studies.
- the technology essentially reduces the transcriptome to a series of randomly fragmented segments of a few hundred nucleotides in length. These molecules are amplified by a process that retains spatial clustering of the PCR products, and individual clusters are sequenced in parallel by one of several technologies.
- Current NGS platforms include the Roche 454 Genome Sequencer, Illumina's Genome Analyzer, and Applied Biosystems' SOLiD. These platforms can analyze tens to hundreds of millions of DNA fragments simultaneously, generate giga-bases of sequence information from a single run, and have revolutionized SAGE and cDNA sequencing technology.
- the 3' tag Digital Gene Expression uses oligo-dT priming for first strand cDNA synthesis, generates libraries that are enriched in the 3' untranslated regions of polyadenylated mRNAs, and produces base cDNA tags.
- DGE Digital Gene Expression
- sequencing methods contemplated herein requires the preparation of sequencing libraries.
- nucleic acid fragments with 5'- and/or 3 '-overhangs are blunt-ended using T4 DNA polymerase that has both a 3 '-5' exonuclease activity and a 5'-3' polymerase activity, removing overhangs and yielding complementary bases at both ends on DNA fragments.
- the T4 DNA polymerase may be provided as a droplet.
- T4 polynucleotide kinase may be used to attach a phosphate to the 5'-hydroxyl terminus of the blunt-ended nucleic acid.
- the T4 polynucleotide kinase may be provided as a droplet.
- the 3' hydroxyl end of a dATP is attached to the phosphate on the 5 '-hydroxyl terminus of a blunt- ended fragment catalyzed by exo-Klenow polymerase.
- sequencing adaptors are ligated to the A-tail.
- T4 DNA ligase is used to catalyze the formation of a phosphate bond between the A-tail and the adaptor sequence.
- end-repairing including blunt-ending and phosphorylation
- sequencing library preparation can involve the production of a random collection of adapter-modified DNA fragments (e.g., polynucleotides) that are ready to be sequenced.
- Sequencing libraries of polynucleotides can be prepared from DNA or RNA, including equivalents, analogs of either DNA or cDNA, for example, DNA or cDNA that is complementary or copy DNA produced from an RNA template, by the action of reverse transcriptase.
- the polynucleotides may originate in double-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA, PCR amplification products, and the like) or, in certain embodiments, the polynucleotides may originated in single-stranded form (e.g., ssDNA, RNA, etc.) and have been converted to dsDNA form.
- dsDNA double-stranded form
- single-stranded form e.g., ssDNA, RNA, etc.
- single stranded mRNA molecules may be copied into double-stranded cDNAs suitable for use in preparing a sequencing library.
- the precise sequence of the primary polynucleotide molecules is generally not material to the method of library preparation, and may be known or unknown.
- the polynucleotide molecules are DNA molecules. More particularly, in certain embodiments, the polynucleotide molecules represent the entire genetic complement of an organism or substantially the entire genetic complement of an organism, and are genomic DNA molecules (e.g., cellular DNA, cell free DNA (cfDNA), etc.), that typically include both intron sequence and exon sequence (coding sequence), as well as non-coding regulatory sequences such as promoter and enhancer sequences.
- the primary polynucleotide molecules comprise human genomic DNA molecules, e.g., cfDNA molecules present in peripheral blood of a subject.
- Preparation of sequencing libraries for some NGS sequencing platforms is facilitated by the use of polynucleotides comprising a specific range of fragment sizes.
- Preparation of such libraries typically involves the fragmentation of large polynucleotides (e.g. cellular genomic DNA) to obtain polynucleotides in the desired size range.
- the method comprises determining whether the patient is at risk of suffering from preeclampsia if the level of expression of the gene measured in the first step deviates with respect to a pre-established threshold level of expression.
- a reference gene expression level can be a “threshold” level or a “cut-off’ level.
- a “threshold level” or “cut-off level” can be determined experimentally, empirically, or theoretically.
- the suitable reference expression levels of the at least one gene according to the invention can be determined by measuring the expression level of said gene in several suitable subjects, and such reference level can be adjusted to specific subject populations.
- a reference level can be linked to non-pregnant subjects with a history of preeclampsia so that comparisons can be made between expression levels in samples of non-pregnant subjects and reference levels for preeclampsia.
- the reference sample may be a pool of samples of endometrial tissue from several individuals.
- the pre-established threshold level of expression corresponding to the at least one gene is determined in a sample from a subject or a pool of subjects which have suffered from preeclampsia.
- the pre-established threshold level of expression of the at least one gene is determined in a sample from a subject or a pool of subjects which have not suffered from preeclampsia.
- a “pre-established threshold level of expression”, as used herein, may refer to the level of expression of at least one gene determined in a non-pregnant subject who suffered from preeclampsia in a previous pregnancy or to the expression level in a subject who was tested 1 to 6 days before the end of the menstrual cycle and which did not develop preeclampsia during a subsequent pregnancy.
- a “pre-established threshold level of expression”, as used herein, may refer to the level of expression of at least one gene determined in non-pregnant subjects who have not suffered preeclampsia in a previous pregnancy.
- a “pre-established threshold level of expression”, as used herein, may also refer to the level of expression of at least one gene determined in non-pregnant healthy subjects.
- a patient which has no symptoms of preeclampsia, but has a high probability to develop clinical symptoms of preeclampsia as pregnancy proceeds is a patient at risk of suffering from preeclampsia.
- a patient which has no symptoms of preeclampsia and has a low probability to develop clinical symptoms of preeclampsia as pregnancy proceeds is not a patient at risk of suffering from preeclampsia.
- the method of the invention allows for a classification of a subject based on the risk of said subject to suffer from preeclampsia to develop clinical signs related to preeclampsia within a defined time interval.
- a defined time interval may refer to the period of pregnancy.
- the method for predicting the risk of suffering from preeclampsia allows the classification or selection of a pregnant patient as i) being at risk of suffering from preeclampsia or ii) not being at risk of suffering from preeclampsia.
- the levels of expression of the at least one gene in a subject in which the risk of suffering from preeclampsia is to be determined can be compared with the pre-established threshold level, and thus be assigned as “increased”, “decreased” or “equal”.
- the expression profile of the genes in the reference sample can preferably be generated from a population of two or more individuals.
- the population for example, can comprise 3, 4, 5, 10, 15, 20, 30, 40, 50 or more individuals.
- a patient may be classified as having the status of being at risk of suffering from preeclampsia based on the deviation of the level of expression of at least one gene with respect to a pre-established threshold level of expression.
- a patient may be classified as having the status of not being at risk of suffering from preeclampsia based on the deviation of the level of expression of at least one gene according to the invention with respect to a pre-established threshold level of expression.
- a deviation in the level of expression of at least one gene according to the invention may refer to an increase in the level of expression as compared with a pre- established threshold level of expression. In some embodiments, a deviation in the level of expression of at least one gene may refer to a reduction in the level of expression as compared with a pre-established threshold level of expression.
- an increase in the level of expression of at least 1.1-fold, 1.5-fold, 5- fold, 10-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the pre-established threshold level of expression is considered as “increased” level.
- a reduction in the level of expression of at least 1.1-fold, 1.5-fold, 5- fold, 10-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the pre-established threshold level of expression is considered as “reduced” level.
- the term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene may relate to a situation where the level of the at least one gene is increased at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% when compared to the corresponding pre-established threshold level of expression.
- the term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene may relate to a situation where the level of expression of the at least one gene is reduced at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% when compared to the corresponding pre-established threshold level of expression.
- Levels can be seen as “equal” to the pre-established threshold level of expression if the levels differ with respect to the pre-established threshold level of expression less than 5%, less than 4%, less than 3%, less than 2%, less than 1%, less than 0.5 %, less than 0.4%, less than 0.3%, less than 0.1%, less than 0.05%, or less.
- the method of the invention is characterized in that the expression level of at least one gene shown in Table 21 is determined. In a preferred embodiment, the method of the invention is characterized in that the expression level of at least one gene shown in Table 4 is determined.
- the deviation in the level of expression of at least one gene according to the invention may refer to a log2fold change.
- the term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene according to the invention may relate to a situation where the level of expression of the at least one gene is reduced a log2fold of at least 1, a log2fold of at least 1.5, a log2fold of at least 2, a log2fold of at least 2.5, a log2fold of at least 3, a log2fold of at least 5, compared with a pre-established threshold level of expression is considered as “reduced” level.
- the method of the invention is characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 3, a log2fold change of at least 2 in the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 9 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 10.
- the in vitro method is characterized in that the expression levels of all genes comprised in Table 3, in Table 8, in Table 9 and/or, in Table 10. In another embodiment, the in vitro method is characterized in that the expression levels of all genes comprised in Table 13.
- the method of the invention is characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 15, a log2fold change of at least 2 in the expression of at least a gene included in Table 16, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 17 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 18.
- the in vitro method is characterized in that the expression levels of all genes comprised in Table 15, in Table 16, in Table 17, in Table 18, in Table 19.
- the invention relates to an in vitro method for determining the risk of suffering from preeclampsia comprises:
- step (ii) Wherein reduced expression levels of expression of the progesterone receptor or of the estrogen receptor 1 in step (i) as compared with a reference expression value, is an indication that the patient is at risk of suffering from preeclampsia.
- progesterone receptor refers to mRNA encoding the isoform B of the progesterone receptor gene and which results from the alternative splicing of the transcript encoded by the progesterone receptor gene which is shown in the HGNC database under accession number 8910, in the NCBI Entrez Gene database under accession number 5241, in the Ensembl database under accession number ENSG00000082175, in the OMIM® database under accession number 607311.
- the progesterone receptor isoform B polypeptide is shown in the UniProtKB/Swiss-Prot database under accession number P06401-1.
- estrogen receptor 1 refers to the gene encoding a nuclear hormone and which is shown in the HGNC database under accession number 3467, in the NCBI database under Entrez Gene number 2099, in the Ensembl database under accession number ENSG00000091831, in the OMIM® database under accession number 133430 and which encodes a polypeptide shown in the UniProtKB/Swiss-Prot database under accession number P03372.
- the biological sample is an endometrial tissue sample.
- the term “prognosis of preeclampsia” may be understood as the prospect of recovery as anticipated from the usual course of preeclampsia or peculiarities of the case. Also, the term “prognosis” may refer to the likely outcome or course of a disease; the chance of recovery or recurrence. Accordingly, the terms “prognosis of preeclampsia” and “risk of suffering from preeclampsia” as used herein may be understood as synonyms.
- the present invention relates to an in vitro method for the prognosis of preeclampsia.
- the method comprises measuring the expression level of at least a gene selected from Table 3, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
- the method comprises determining whether the patient is suffering from preeclampsia or has a bad prognosis if the level of expression of the gene measured in the first step deviates with respect to a pre-established threshold level of expression.
- (i) comprises the measurement of the expression level of at least a gene selected from Table 4, or any combination thereof.
- step (i) Entering into the computer the reference expression levels of the genes which are measured in step (i);
- Methods of the invention can be performed using software, hardware, firmware, hardwiring, or combinations of any of these.
- the invention provides a method wherein a patient is selected based on showing an increased risk of suffering preeclampsia, said patient is then monitored in any subsequent pregnancy for the appearance of the symptoms of preeclampsia and then, if preeclampsia is detected, the patient is treated with a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia.
- the invention relates to a method for predicting the risk of suffering preeclampsia and treating preeclampsia in a subject which comprises:
- the invention relates to a therapy useful in the treatment of preeclampsia for use in the treatment of a subject suffering from preeclampsia, wherein the patient was identified as having high risk of suffering preeclampsia by any of the methods according to the invention and then further selected by detecting the appearance of preeclampsia during a pregnancy subsequent to the prediction of the risk of suffering preeclampsia.
- preeclampsia can be detected in the patients identified as having high risk of suffering preeclampsia by detecting of any of the following:
- Anticonvulsant medications If your preeclampsia is severe, your doctor may prescribe an anticonvulsant medication, such as magnesium sulphate, to prevent a first seizure.
- an anticonvulsant medication such as magnesium sulphate
- the invention relates to a kit, package or device that contains reagents adequate for implementing any of the methods of the invention. It will be understood that, depending on the nature of the method, the reagents adequate for its implementation will vary.
- kit is understood as a product containing the different reagents required for carrying out the methods of the invention packaged such that it allows being transported and stored.
- the materials suitable for the packaging of the components of the kit include glass, plastic (polyethylene, polypropylene, polycarbonate, and the like), bottles, vials, paper, sachets, and the like. Where there are more than one component in a kit they may be packaged together if suitable or the kit will generally contain a second, third or other additional container into which the additional components may be separately placed. However, in some embodiments, certain combinations of components may be packaged together comprised in one container means.
- a kit can also include a means for containing any reagent containers in close confinement for commercial sale.
- kits according to the invention can also comprise one or more reagents for preparing crude cell lysates and/or reagents for extracting, isolating and/or purification of nucleic acids from a sample.
- Additional components can comprise particles with affinity for nucleic acids and/or solid supports with affinity for nucleic acids, one or more wash buffers, binding enhancers, binding solutions, polar solvents, alcohols, elution buffers, filter membranes and/or columns for isolation of DNA/RNA.
- a kit may further comprise reagents for downstream processing of an isolated nucleic acid and may include without limitation at least one RNase inhibitor; at least one cDNA construction reagents (such as reverse transcriptase); one or more reagents for amplification of RNA, one or more reagents for amplification of DNA including primers, reagents for purification of DNA, probes for detection of specific nucleic acids.
- the kits of the invention can contain instructions for the simultaneous, sequential, or separate use of the different components that are in the kit.
- the kit comprises primers or probes adequate for the detection of the expression levels of one or more of the genes, the expression levels of which are determined in the any of the methods according to the invention.
- primer refers to oligonucleotides that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the primer within a sequence of the target polynucleotide sequence.
- a primer can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides.
- a primer can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to the hybridized portion of the target polynucleotide sequence.
- Oligonucleotides useful as primers may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts. (1981) 22: 1859-1862, using an automated synthesizer, as described in Needham-Van Devanter et al, Nucleic Acids Res. (1984) 12: 6159-6168. Primers are useful in nucleic acid amplification reactions in which the primer is extended to produce a new strand of the polynucleotide.
- Primers can be readily designed by a skilled artisan using common knowledge known in the art, such that they can specifically anneal to the nucleotide sequence of the target nucleotide sequence of the at least one biomarker provided herein.
- the 3' nucleotide of the primer is designed to be complementary to the target sequence at the corresponding nucleotide position, to provide optimal primer extension by a polymerase.
- probe refers to oligonucleotides or analogs thereof that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the probe within a sequence of the target polynucleotide sequence.
- exemplary probes can be, for example DNA probes, RNA probes, or protein nucleic acid (PNA) probes.
- a probe can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides.
- a probe can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to hybridized portion of the target polynucleotide sequence. Probes can also be chemically synthesized according to the solid phase phosphoramidite triester method as described above. Methods for preparation of DNA and RNA probes, and the conditions for hybridization thereof to target nucleotide sequences, are described in Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition. Cold Spring Harbor Laboratory Press, 1989, Chapters 10 and 11.
- the reagents adequate for the determination of the expression levels of one or more genes comprise at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% of the total amount of reagents adequate for the determination of the expression levels of genes forming the kit.
- Example 1 Material and methods Example 1.1. Study Design
- NGS next generation sequencing
- Samples were collected from women aged 18-42 without any medical condition who had been pregnant 1-8 years earlier. All patients had regular menstrual cycle (26-32 days), with no underlying gynecological pathologic conditions, and had not received hormonal therapy in the 3 months preceding sample collection. After the inclusion criteria were applied, endometrial biopsies were obtained by pipelle (Genetics Hamont-Achel, Belgium) under sterile conditions in the late secretory (LS) phase (cycle days 22-32). Specimens were kept in preservation solution until processing.
- RNA from endometrial biopsies was isolated using QIAsymphony RNA kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. RNA concentrations were quantified using a Multiskan GO spectrophotometer (Thermo Fisher Scientific, Waltham, CIS) at a wavelength of 260 nm. Integrity of the total RNA samples was evaluated by the RNA integrity number (RIN) and DV200 metrics using an Agilent high-sensitivity RNA ScreenTape in a 4200 TapeStation system (Agilent Technologies Inc., Santa Clara, CA). Samples used for the global RNA-seq showed RIN values ranging from 4.9 to 9.2.
- Example 1.4 Global RNAseq library preparation and transcriptome sequencing
- the mRNAs were selected from the total RNAs by purifying the poly-A containing molecules using poly-T oligo attached to magnetic beads.
- the RNA fragmentation, first and second strand cDNA syntheses, end repair, single ‘A’ base addition, adaptor ligation, and PCR amplification were performed according to the manufacturer’s protocol.
- the average size of the cDNA libraries was approximately 350 bp (including the adapters).
- the cDNA libraries were validated for RNA integrity and quantity using an Agilent 4200 TapeStation system (Agilent Technologies Inc., Santa Clara, CA) before pooling the libraries.
- the pool concentration was quantified by a qPCR using the KAPA Library Quantification Kit (Kapa Biosystems Inc.) before sequencing in a NextSeq 500/550 cartridge of 150 cycles (Illumina, San Diego, CA). Indexed and pooled samples were sequenced 150-bp paired-end reads by on the Illumina NextSeq 500/550 platform, according to Illumina library protocol.
- Example 1.6 Transcriptomic fingerprinting definition and validation
- genes with assigned EntrezID with an FDR cut-off of 0.05 and an expression >4-fold higher in the sPE vs. control training set samples were selected to define a fingerprint associated with DD in sPE.
- genes with assigned EntrezID with an FDR cutoff of 0.05 and an expression >1.4- fold higher in the sPE vs. control training set samples were selected to define a fingerprint associated with DD in sPE.
- Targeted analysis of fingerprinting genes was performed using the validation set of samples. PCA and unsupervised hierarchical clustering with a Canberra distance based on gene signature were performed comparing sPE to control specimens. Custom scripts are available on GitHub at https:// github.com/mclemente-igenomix/garrido_et_al_2021.
- Biological process in which those differentially expressed genes (DEGs) are involved were studied.
- edgeR GO analyses can be conducted using the goana.
- an FDR cutoff of 5% is used when extracting DE genes and for log2FC we use cut-off value of 1 [UP, log2FC>l and DOWN; log2FC ⁇ (-1)].
- the ontology domain that GO term belongs to is biological process (BP).
- BP biological process
- the input genes were those 120 included in the fingerprinting.
- the p-value adjustment method was FDR with a cut-off of 0.05 (FDR ⁇ 0.05). Custom scripts are available on GitHub at https://github.com/mclemente-igenomix/garrido_et_al_2021.
- ISM1_RV AGAACTCGCTTTTGCAGCTC
- WNT5A RV CCGATGTACTGCATGTGGTC cDNA was generated from 400 ng of RNA using the Superscript VILO cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, US).
- the template cDNA was diluted 5 in 20 and 1 pL was used in each PCR reaction.
- Real-time PCR was performed in duplicate in 10 pL using commercially validated Kapa SYBR fast qPCR kit (Kapa biosystems Inc, Basilea, Switzerland) and the Lightcycler 480 (Roche Molecular Systems, Inc, Pleasanton, CA) detection system. Samples were run in duplicate along with appropriate controls (i.e. no template, no RT).
- Clinical data are expressed as mean ⁇ standard error mean (SEM). Clinical data were evaluated by two statistical methods: Student's t-test (Table 2) and Wilcoxon test (Table 11) for comparisons between sPE and control samples. Statistical significance was set at P-value of ⁇ 0.05. Differential expression analysis was performed using the R package edgeR.
- Example 2 First analysis of the global transcriptional signature of defective decidualization in vivo from sPE patients
- Example 2.1 Global transcriptional signature of defective decidualization in vivo from sPE patients
- the up-regulated category identifies molecules involved in amino acid metabolic/catabolic processes ( GGT3P , 11)02, and PRODH ), transport and oxidoreductase activity.
- Example 2.2 In vivo vs in vitro decidualization fingerprinting
- ERP27, CHODL , and PRUNE2 down-regulated [e.g. ISM 1, MEST,MFAP2 , and REEP2 ].
- Example 2.3 Identification of molecular fingerprinting encoding in vivo decidualization defect in sPE patients
- fingerprinting genes representative of the altered pathways in sPE such as, IL6 and TNF regulating the acute inflammatory response, MMP3 and MMP1 participating in the extracellular matrix disassembly, POSTN and REN as affected regulators of the systemic arterial blood pressure and PRL, IHH and ICAM1 implicated in the down-regulated response to hormone (Figure 3C).
- Up-regulated biological processes were associated with metabolism process, nervous system, interaction between organisms, and negative regulation of chorionic trophoblast cell proliferation. Functional analysis evidenced that the 166 DEGs between sPE vs. control pregnancies included in the fingerprinting were implicated in pathways related with decidualization corroborating the maternal contribution to sPE.
- Example 2.5 Validation of the DD-fingerprinting to segregate sPE and control samples
- PCA principal component analysis
- Progesterone acting through its receptor (PR) is the key hormone modulating the decidualization.
- the fingerprinting that undergoes the defective decidualization phenotype of patients with a previous sPE includes 106 enriched genes in the endometrium tissue defined by the human protein atlas and 50% of them were associated with PR (or PR binding site within EPR, 10 kb or 25 kb)(Figure 5A).
- PR or PR binding site within EPR, 10 kb or 25 kb
- IHH expression was found downregulated, which affected the PR signalling pathway in the stromal compartment compromising decidualization, and consequently, affecting processes involved in immune system deregulation and endothelial dysfunction (Figure 5F).
- IHH expression was found downregulated, which affected the PR signalling pathway in the stromal compartment compromising decidualization, and consequently, affecting processes involved in immune system deregulation and endothelial dysfunction (Figure 5F).
- PRL and WNT regulators and effectors such as FGF1, FJX1, SFRP1, NKD1— and extracellular matrix degradation [e.g. MMP1, MMP9 and MMP11 ⁇ .
- Example 2.7 Assays for assessing the proper time for collecting the samples.
- the ERA test evaluates endometrial receptivity detecting the optimal day for the embryo transfer that is specific for each women.
- the protocol indicates to take an endometrial biopsy during the window of implantation (WOI), in a natural cycle correspond to 19-21 days of the menstrual cycle and in a hormone replacement therapy (HRT) cycle after five days of progesterone administration (120 hours from the first progesterone intake; P+5).
- WOI window of implantation
- HRT hormone replacement therapy
- the raw sequencing genes among the 89 samples were 56,638 and after normalization the number of genes included in the analysis was 19,941.
- PCA principal component analysis
- Figure 9 showed the dots colour based of the disease status: control or preeclampsia. While, we can see at the Figure 9, that the disease is not an important factor for grouping samples. They are mixed together.
- our work demonstrates that the samples should be collected during late secretory menstrual cycle (1 to 6 before menstrual cycle ends) to detect transcriptional differences between control and preeclampsia that let successful classify the samples in the two groups.
- a gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.
- a gene deregulation could be responsible of the physiological differences associated with severe preeclampsia.
- Adjusted p-value with a cut-off of 0.05 was “FDR”.
- Level of deregulation (sPE vs control) is referred as log2 -fold-change (logFC).
- logFC log2 -fold-change
- Table 6 shows the number of DE genes obtained for each cut-off of log2FC.
- the DE genes were used to plot the dendrogram and principal component analysis (PCA). These plots allow us to observe the similarity or dissimilarity among samples based on the DE genes. The expected result was to obtain two separated groups (sPE and control). That distribution of samples would mean that the DE genes enable to classify samples in sPE or control.
- PCA principal component analysis
- Example 3 Second analysis of the global transcriptional signature of defective decidualization in vivo from sPE patients 3.1. Endometrial transcriptome alterations during decidualization in sPE
- RNA-seq global RNA sequencing
- Table 11 Maternal and neonatal characteristics for endometrial donors.
- Table 12 The 593 statistically differential expressed genes (FDR ⁇ 0.05) with at least 1.2-fold change (FC > 1.2) in sPE vs control cases obtained from RNA-seq analysis.
- Downregulated transcripts include those involved in decidualization, such as MMP3, PRL, IL- 6, and IHH; and genes associated with signaling (e.g., NR4A3 and IL8), growth factors (e.g., FGF1 and FGF7), angiogenesis (e.g., EDN2 and TMEM215), and immune response (CCL20, CXCL3, and IGHG1).
- Upregulated genes are involved in amino acid metabolic/catabolic processes (ID02 and CAPN3), transport, and oxidore- ductase activity.
- GO analysis of the gene signature associated with DD in sPE identified 151 enriched biological processes downregulated (FDR ⁇ 0.05). These pathways were associated with cell cycle, DNA damage response, cell signaling, cellular response, cell motility, extracellular matrix, immune response, and reproductive process (Figure 12B). All are hallmarks of impaired decidualization and sPE pathogenesis.
- PC A showed that sPE and control samples clustered separately in two groups, except for three control samples (C20, C21, and C22) (Figure 13A). High variance between groups was effectively captured in the first two principal components. Unsupervised hierarchical clustering analysis confirmed that gene fingerprinting effectively segregated the two groups: one encompassing mainly controls and the other mainly sPE samples ( Figure 13B). The same three controls clustered with the sPE group, recreating the PC A results.
- hTFtarget Human Transcription Factor Targets
- Clustering revealed three main modules based on their connectivity degree, with functionally relevant genes involved in gland morphogenesis, cell migration, extracellular matrix organization, stromal cell differentiation, cellular response to DNA damage stimulus, and regulation of cell cycle.
- the hub genes were determined by overlapping the top 10 genes obtained using two topological analysis methods in the cytoHubba plugin (Chin et al., 2014), MCC, and MNC. Five genes were selected, all of which were downregulated.
- both ER1 and PR were strongly embedded in the network and highly connected with DD fingerprinting, highlighting the interaction of hormonal receptors with notable decidualization mediators such as IHH and MSX2 validated by RT-qPCR ( Figure 14C and D).
- the interactome demonstrated a direct interaction between ER1 and PR.
- a gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.
- a gene deregulation could be responsible of the physiological differences associated with severe preeclampsia.
- logFC log2 -fold-change
- the DE genes were used to plot the dendrogram and principal component analysis (PCA). These plots allow us to observe the similarity or dissimilarity among samples based on the DE genes. The expected result was to obtain two separated groups (sPE and control). That distribution of samples would mean that the DE genes enable to classify samples in sPE or control.
- PCA principal component analysis
- Example 3.6 Selection of differentially expressed (DE) genes identified in the second analysis but not identified in the first analysis A gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.
- DE differentially expressed
- the parameters used to consider a gene as DE were: ⁇ Adjusted p-value with a cut-off of 0.05.
- the adjustment method was “FDR”.
- Table 21 shows the DE genes forming part of the defective decidualization fingerprinting that were identified in the second analysis but not in the second analysis (47 genes) Table 21 ( ⁇ oik' S mbol PViiliio I (
- CDH24 64403 5,04321E-05 0,019683522 -1,4577474 -3
- RNASEH2A 10535 0,000127691 0,030030396 -1,1237793 -2
- TIPIN 54962 0,000179616 0,036609649 -1,0144111 -2
- FC indicates downregulated genes calculated as -POWER(2,-logFC)
- In vitro method for the diagnosis and/or prognosis of preeclampsia which comprises: a. Measuring the expression level of at least a gene selected from Table 3, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
- In vitro method according to aspect 1, which comprises: a.
- in vitro method which comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2fold change of at least 1 for the expression of at least a gene included in Table 3, a log2fold change of at least 2 for the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 for the expression of at least a gene included in Table 9 or a log2 fold change of at least 3 for the expression of at least a gene included in Table 10
- In vitro method according to any of the previous aspects, which comprises: a.
- a deviation in the level of expression of the genes measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
- Kit for implementing any of the methods according to aspects 1 to 8 which comprises: a. Reagents for measuring the level of expression of at least a gene selected from Table 3, or any combination thereof, b. Tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle.
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Health & Medical Sciences (AREA)
- Organic Chemistry (AREA)
- Wood Science & Technology (AREA)
- Analytical Chemistry (AREA)
- Zoology (AREA)
- Genetics & Genomics (AREA)
- Engineering & Computer Science (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Microbiology (AREA)
- Molecular Biology (AREA)
- Biotechnology (AREA)
- Biophysics (AREA)
- Physics & Mathematics (AREA)
- Biochemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The present invention refers to an in vitro method for determining the risk of suffering from preeclampsia. The method of the invention is particularly characterized in that it is based on determining the level of expression of genes measured in a biological sample obtained from the patient 1 to 6 days before the end of the menstrual cycle.
Description
IN VITRO METHOD FOR DETERMINING THE RISK OF SUFFERING FROM
PREECLAMPSIA
FIELD OF THE INVENTION
The present invention refers to the medical field. Particularly, it refers to an in vitro method for the prognosis of preeclampsia as well as for determining the risk of suffering from preeclampsia. The method of the invention is particularly characterized in that it is based on determining the level of expression of genes measured in an endometrial tissue sample obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.
STATE OF THE ART
Preeclampsia (PE) is a severe late pregnancy complication specific to humans. It is the second leading cause of maternal mortality in USA affecting ~8% of first-time pregnancies contributing significantly to neonatal mortality and morbidity. This condition is characterized by the new onset of hypertension, proteinuria and other signs of maternal vascular damage. In severe preeclampsia (sPE) cases women suffers a further elevation of blood pressure (systolic >160 mm Hg or diastolic of >100 mm Hg) or any of the following: thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema and the new onset of cerebral or visual disturbances.
The placenta plays a central role in PE pathophysiology with deficient cytotrophoblast (CTB) invasion of uterine decidua and spiral arterioles producing an incomplete endovascular invasion and altered uteroplacental perfusion. The unsolved question is why shallow CTB invasion occurs. Pregnancy health is determined not only by the embryo -and the placenta- but also by the quality of the maternal decidua, where CTB invasion and remodeling of the maternal spiral arteries occurs. The contribution of the decidua to the etiology of PE, sPE, and placenta accrete has been suggested.
Decidualization is the remodelling of the maternal endometrium initiated after ovulation necessary for adequate trophoblast invasion and subsequent placentation. The formation of the decidua in humans is a conceptus-independent process driven by the progesterone secreted after ovulation and local cyclic Adenosine Monophosphate (cAMP) that through progesterone
receptor activation stimulates synthesis of a complex network of intracellular and secreted proteins. Endometrial decidualization involves secretory transformation of the uterine glands, influx of specialized immune cells, vascular remodeling, and the morphological, biochemical and transcriptional reprogramming of the endometrial stromal compartment. Morphologically, is characterized by the transformation of elongated fibroblast-like endometrial stromal cells (ESCs) into enlarged polygonal/round cells shaped by a complex intracellular cytoskeleton rearrangement. Decidualized ESCs secrete biomarkers such as prolactin (PRL) and insulin-like growth factor binding protein- 1 (IGFBP1). Recently, we characterized the transcriptomics of the decidualization process at single cell resolution, discovering that is initiated gradually after ovulation with a direct interplay between stromal fibroblasts and lymphocytes collaborating in the widespread decidualized features by the end of the menstrual cycle.
Defective decidualization entails the inability of the endometrial compartment to undertake tissue differentiation leading to aberrations in trophoblast invasion and placentation, compromising pregnancy health like in sPE. Most of our knowledge about this function in health and disease has been generated from in vitro model systems.
Thus, despite the developments that have been made in this technical field there is still an unmet medical need of finding reliable methods for the identification of sPE. The present invention is focused on solving this problem and a specific preconceptional endometrial transcriptomic signature is herein presented, which is associated with defective decidualization (DD) that might contribute to sPE.
SUMMARY OF THE INVENTION
As explained above, the present invention refers to an in vitro method for the prognosis of preeclampsia. The present invention also refers to an in vitro method for determining the risk of suffering from preeclampsia in a subject.
The present invention also relates to an in vitro method for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, wherein early diagnosis is preconception diagnosis, i.e. diagnosis that is carried out before conception.
With the objective of reaching that purpose, we initially performed a global transcriptional profiling of endometrial tissue from patients in whom sPE developed in a previous pregnancy using samples which had been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.
First results using the data analysis approach (a) identified 859 genes differentially expressed in sPE vs control cases. A molecular DD-fmgerprinting including 166 genes was defined associated to the development of sPE, which was tested in an independent cohort of samples. A second analyses using the data analysis approach (b) of the transcriptional profile revelaed 593 genes differentially expressed in sPE vs control cases and a molecular DD-fmgerprinting including 120 genes was defined associated to the development of sPE, which was tested in an independent cohort of samples.
Our analysis revealed the down-regulation of progesterone receptor and its close relation with a high proportion of DD-fmgerprinting genes in sPE. The analysis revealed the down- regulation of estrogen receptor- 1 and its close relation with a high proportion of DD- fmgerprinting genes in sPE patients.
Together, our results suggest that the signature encoding defective decidualization defined in the present invention can be used in a preconceptional stage for in assessing the risk of sPE and the development of therapies focused on improving decidualization.
So, the first embodiment of the present invention refers to an in vitro method for the prognosis of preeclampsia which comprises: a) Measuring the expression level of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle; and b) wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre- established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In another embodiment, the invention refers to a method as defined above wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
In this sense, it is important to consider that the special technical feature which defines the contribution that the invention makes over the prior art, and consequently confers unity to the present invention, is measuring the level of expression of the genes in a biological sample, preferably endometrial tissue, obtained from the patient 1 to 6 days before the end of the menstrual cycle. This means that any gene included in Table 3 could be used for the prognosis of preeclampsia as far as the expression of the gene is measured in a sample, preferably endometrial tissue, obtained from the patient 1 to 6 days before the end of the menstrual cycle. In this sense, kindly refer to Example 2.7, wherein it is concluded that in the case that biological sample is endometrial tissue, the samples should be collected during late secretory menstrual cycle (1 to 6 before menstrual cycle ends) to detect transcriptional differences between control and preeclampsia that let successful classify the samples into the two groups.
In a preferred embodiment, the method of the invention comprises determining that the patient has a bad prognosis based on a log2fold change of at least 1 for the expression of at least a gene included in Table 3, a log2fold change of at least 2 for the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 for the expression of at least a gene included in Table 9 or a log2 fold change of at least 3 for the expression of at least a gene included in Table 10. In another embodiment, the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
In a preferred embodiment, the method of the invention comprises: a) Measuring the expression level of all genes comprised in Table 10, or in Table 9, or in Table 8, or in Table 3, or in Table 4, or in Table 3, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b) wherein a deviation in the level of expression of the genes measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In another embodiment, the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
In a preferred embodiment, the method of the invention comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2fold change of at least 1 for the expression of all genes included in Table 3, a log2fold change of at least 2 for the expression all genes included in Table 8, a log2 fold change of at least 2.5 for the expression all genes included in Table 9 or a log2 fold change of at least 3 for the expression of all genes included in Table 10. In another embodiment, the invention refers to the method as defined in this paragraph wherein the method is used for the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
In a preferred embodiment, the method of the invention comprises: a) Measuring the expression level of progesterone receptor in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b) wherein the determination of a lower the level of expression in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
In a preferred embodiment, the method of the invention comprises: a) Entering into the computer the level of expression of the genes obtained from healthy control subjects; b) entering into the computer the level of expression of the genes obtained in the step a) of the previous claims; c) producing a score which is displayed on the device; and d) determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with a pre-established threshold level of expression determined in healthy control subjects.
In a preferred embodiment, the biological sample is an endometrial tissue sample.
The second embodiment of the present invention refers to the use in vitro use of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, for the prognosis of preeclampsia, in a biological sample, preferably endometrial tissue, which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle. In another embodiment, the invention refers to the use as defined in this paragraph wherein the use os aimed at the diagnosis of preeclampsia, wherein the diagnosis is preferably early diagnosis, most preferably preconception diagnosis.
The third embodiment of the present invention refers to a kit for implementing the method of the invention which comprises: a) Reagents for measuring the level of expression of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, and b) tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle.
The fourth embodiment of the present invention refers to the use of the above defined kit for the prognosis of preeclampsia.
The fifth embodiment of the present invention refers to an in vitro method for obtaining information from patients who may be suffering from preeclampsia which comprises: a) Measuring the expression level of at least a gene selected from Table 3, Table 4, Table 8, Table 9 or Table 10, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, preferably 1 to 3 days before the end of the menstrual cycle.
The fifth embodiment of the present invention refers to a method for treating preeclampsia which comprises the administration of a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once the patient has been diagnosed by following the above explained method of the invention. Treatments which could be used for treating preeclampsia include:
• Medications to lower blood pressure. These medications, called antihypertensives, are used to lower your blood pressure if it's dangerously high. Blood pressure in the 140/90 millimetres of mercury (mm Hg) range generally isn't treated.
• Corticosteroids. If you have severe preeclampsia or HELLP syndrome, corticosteroid medications can temporarily improve liver and platelet function to help prolong your pregnancy. Corticosteroids can also help your baby's lungs become more mature in as little as 48 hours, an important step in preparing a premature baby for life outside the womb.
• Anticonvulsant medications. If your preeclampsia is severe, your doctor may prescribe an anticonvulsant medication, such as magnesium sulphate, to prevent a first seizure.
For the purpose of the present invention the following terms are defined:
• The term "comprising" means including, but it is not limited to, whatever follows the word "comprising". Thus, use of the term "comprising" indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.
• By "consisting of’ means including, and it is limited to, whatever follows the phrase “consisting of’. Thus, the phrase "consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present.
A further aspect of the invention includes an in vitro method for determining the risk of suffering from preeclampsia in a subject, which comprises:
(i) Measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, Table 3 or Table 12 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and
(ii) wherein a deviation in the level of expression of the gene measured in step a) as compared with a reference expression level, is an indication that the patient is at risk of suffering from preeclampsia.
In another aspect, the invention relates to an in vitro method for determining the risk of suffering from preeclampsia, which comprises:
(i) Measuring the expression level of the progesterone receptor or of the estrogen receptor 1 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and
(ii) Wherein reduced expression levels of expression of the progesterone receptor or of the estrogen receptor 1 in step (i) as compared with a pre-established threshold level of expression, is an indication that the patient is at risk of suffering from preeclampsia.
In another aspect, the invention relates to a method for predicting the risk of suffering preeclampsia and treating preeclampsia in a subject which comprises:
(i) Determining the risk of suffering from preeclampsia in said subj ect by a method according the invention,
(ii) Monitoring the subjects which have been determined in step (i) to be at high
risk of suffering preeclampsia in order to detect the appearance of preeclampsia and
(iii) Administering to said subject a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once preeclampsia has been detected.
In another aspect, the invention relates to an in vitro use of the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, Table 3 or Table 12 for determining the risk of suffering from preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
In another aspect, the invention relates to a kit for implementing any of the methods according to the invention, which comprises:
(i) Reagents for measuring the level of expression of at least one gene selected from the genes shown in Table 20, in Table 3, in Table 12 or any combination thereof and
(ii) Tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle.
In yet another aspect, the invention relates to the use of the kit according to the invention for determining the risk of suffering from preeclampsia.
Description of the figures
Figure 1. Global transcriptomics RNAseq results revealed 859 DEGs in sPE. (A) Schematic drawing of the study design used to identify and validate the DD-fmgerprinting in sPE. (B) The statistical significance versus gene expression fold change is displayed as a volcano plot drawing the global RNA-seq results. Threshold indicates: pval-FC (FDR<0.05 and log2-fold- change>l); pval (green dots; FDR<0.05 and log2-fold-change>l); FC (blue dots; FDR>0.05 and log2-fold-change>l) and none (purple dots; FDR>0.05 and log2-fold-change>l). (Q Heatmap showing the 25 most highly up-regulated and down-regulated genes (total= 859;) of control vs. sPE patients.
Figure 2. In vitro vs in vivo decidualization comparison. (A) Venn diagram displaying the number of common genes between previous vitro (left) and current in vivo approach (right).
Eighteen genes are overlapping in both approaches. {B) Box plot showing the average expression of the 18 common genes between control (blue boxes) and sPE (orange boxes). ( The fold change between control and sPE was assessed by RT-qPCR (grey bars) and by sequencing (green bars) for 5 transcripts from the 18 transcripts in common in both approaches. (D) Correlation plot for RT-qPCR and RNA seq (method= Pearson, R = 0.99, p = 0.001). RT- qPCR values are expressed Mean± SE. *** P<0.001.
Figure 3. sPE-DD fingerprinting composed by 166 DEGs that are involved in remarkable biologic process. (A) Volcano plot showing the downregulated (blue) and upregulated (red) genes in sPE from the DD-fmgerprinting. Each point represents each gene. Grey points are the not selected genes obtained in the global RNAseq. (B) The five most highly downregulated biological process for each major category of process {red, Immune response; yellow, signaling; green, Extracellular matrix;Z>/we, Cell motility; grey , Blood pressure regulation; purple, Reproductive process) (Q Clustering of DD-fmgerprinting genes within terms is shown for: acute inflammatory response, extracellular matrix disassembly, regulation of systemic blood pressure, response to hormone.
Figure 4. Validation of the DD-fmgerprinting in sPE. (A) Principal component analysis (PCA) based on 166 genes included in the fingerprinting in the training set. Each sample is represented in the Figure as a colored point {blue, Control; orange, sPE). {B) Heatmap dendrogram of expression of the 166 genes included in the final fingerprinting for each sample of the training set (control, n=12; sPE, n=17). (Q Principal component analysis (PCA) based on the fingerprinting in the test set. Each sample is represented in the Figure as a colored point {blue, Control; orange, sPE). (/)) Heatmap dendrogram of expression of the 166 genes included in the final fingerprinting for each sample of the test set (control, n=4; sPE, n=7).
Figure 5. Progesterone receptor is highly connected with DD-fmgerprinting in sPE. {A) Venn diagram displaying the number of genes included in the fingerprinting which are predominantly endometrium expressed and associated with PR, and its overlapping. Genes associated with PR refers to genes with a PR binding site or its expression change if PGR is silenced. {B) Over representation analysis of biological process for the subset of fingerprinting genes which are predominantly expressed in endometrium and associated with PGR (performed using ConsensuspathDB). (Q Gene expression levels of PR, PR-A, and PR-B were assessed for control vs. sPE by RT-qPCR (grey bars, Control; green bars, sPE). RT-qPCR values are expressed Mean± SE. *** P<0.001, ** P<0.01. (D) Progesterone-decidualization network in
control pregnancy including the interaction of immune response and endothelium. (E) Progesterone-decidualization network in sPE pregnancy.
Hypothetical network that could link decidualization failure and progesterone receptor. Molecules represented are codified by genes included in the fingerprinting of in vivo decidualization {blue arrow , Downregulated gene; red arrow , upregulated gene). ART and WNT are intermediate genes. PR (Progesterone receptor).
Figure 6. Transcriptomic analysis based on gestational age at delivery of control samples. (A) PCA based on 18476 genes kept after filtering out lowly expressed genes. (B) Volcano plot. Volcano plot threshold at the legend indicates: none (do not have DE genes neither high fold- change); fc (high fold-change, but not DE genes). Plot based on 728 genes labeled as fc (blue) and 17748 genes labeled as none (purple).
Figure 7. Validation of RNA-seq results with nine relevant genes. {A) Boxplot showing the expression patterns of the nine genes selected obtained in RNA-seq for control (blue boxes) and sPE (orange boxes) group. ( B ) RT-qPCR (grey bars) validating the sequencing results (green bars). RT-qPCR values are expressed Mean± SE. *** P<0.001.
Figure 8. PCA based on normalized gene expression. Dot colour represents the endometrial receptivity analysis (ERA) diagnostic for all samples (control and sPE).
Figure 9. PCA based on normalized gene expression. Dot colour represents control (purple) or preeclamptic (yellow) samples.
Figure 10. Global RNA-seq transcriptomic results revealed 593 differentially expressed genes (DEGs) in severe preeclampsia (sPE) vs. control samples. (A) Schematic drawing of the study design used to identify and validate defective decidualization (DD) fingerprinting in sPE. (B) Statistical significance (-loglO FDR) vs. gene expression log2 fold change (FC) is displayed as a volcano plot of global RNA-seq results. Label indicates: downregulated in sPE (blue dots); upregulated in sPE (red dots); not significant genes (grey dots). (C) Heatmap showing the 25 most upregulated and downregulated genes (total = 593; Figure 1 — source data 1) of control vs. sPE samples.
Figure 11. Defective decidualization (DD) transcriptomics in vitro vs. in vivo. (A) Common genes between previous in vitro (left) and current in vivo approaches analyzing decidualization (right). Nine genes overlap in both approaches. (B) Box plot showing the average expression of the nine common genes between control (blue boxes) and severe preeclampsia (sPE) (orange boxes) samples. (C) From the 593 differentially expressed genes (DEGs) obtained by global
RNA-seq, a subset of 263 DEGs were identified as genes with a human endometrial stromal cell (hESC) origin using the scRNA-seq data published by Wang et al., 2020.
Figure 12. Severe preeclampsia defective decidualization (sPE-DD) fingerprint composed of 120 differentially expressed genes (DEGs). (A) Volcano plot showing downregulated (blue) and upregulated (red) genes in sPE from the DD fingerprint. Each point represents one gene; gray points are the rest of the genes obtained in the global RNA-seq analysis. (B) The three most highly downregulated biological process for each major category (red, cell cycle; yellow, DNA damage response; green, cell signaling; blue, cellular response; gray, cell motility; purple, extracellular matrix; pink, immune response; brown, reproductive process). Enrichment index was calculated by -log(p-value). (C) Clustering of DD fingerprint genes shown for reproductive process, response to bacterial molecules, extracellular matrix organization, regulation of receptor signaling, and response to hormones.
Figure 13. Validation of the defective decidualization (DD) fingerprint in severe preeclampsia (sPE). (A) Principal component analysis (PCA) based on 120 genes included in the fingerprinting in the training set. Each sample is represented as a colored point (blue, control; orange, sPE). (B) Heatmap dendrogram of expression of the 120 genes included in the final fingerprinting for each sample of the training set (control, n = 12; sPE, n = 17). (C) PCA based on the fingerprinting in the test set. Each sample is represented as a colored point (blue, control; orange, sPE). (D) Heatmap dendrogram of expression of the 120 genes included in the final fingerprinting for each sample of the test set (control, n = 4; sPE, n = 7).
Figure 14. Estrogen receptor 1 (ER1) and progesterone receptor-B (PR-B) are linked to defective decidualization (DD) fingerprinting in severe preeclampsia (sPE). (A) Venn diagram displaying genes included in the fingerprinting (120) predominantly expressed in the endometrium based on Human Protein Atlas data that overlap with genes modulated by ESR1 described by Okur et al., 2016 (58) and genes associated with PGR silencing described by Mazur et al., 2015 (35). (B) Network showing the connections between proteins codified by DD fingerprinting and the hormonal receptors, ER1 and PR. Shapes indicate different clusters established by String k-means method. Squares, cluster involved in gland morphogenesis and cell migration; circles, cluster involved in extracellular matrix organization and stromal cell differentiation; hexagons; cluster involved in cellular response to DNA damage and regulation of cell cycle. Color gradient indicate gene expression in terms of log2FC. Hub genes are shown with an asterisk. (C-H) Gene expression levels of IHH, MSX2, ESR1, PGR, PGR- A, and PGR- B assessed for sPE (n= 13) vs. controls (n=9) by RT-qPCR (gray bars, control; green bars,
sPE). RT-qPCR values are expressed as mean± SE. *** p<0.001, ** p<0.01, *p<0.05. (I-J) Tissue sections of control (n=4) and sPE (n=4) endometrium during late secretory phase were immunostained with antibody against ER1 or PR. Nuclei were visualized with DAPI. Scale bar: 50 mM
Detailed description of the invention
The present invention relates to an in vitro method for determining the risk of suffering from preeclampsia. The authors of the invention have found that the risk of suffering from preeclampsia in a patient can be determined by measuring the level of expression of at least one gene in a biological sample of a patient obtained from the patient 1 to 6 days before the end of the menstrual cycle, and comparing this expression level with a reference expression level. The method of the invention allows the identification of women at risk of developing preeclampsia, This may be used to the application of preventive prophylactic measures, medical supervision, medication and treatments before and during pregnancy to reduce maternal and fetal morbidity and mortality.
In vitro method for determining the risk of suffering from preeclampsia
In one aspect, the invention relates to an in vitro method for determining the risk of suffering from preeclampsia, which comprises:
(i) Measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, in Table 3 or in Table 13 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and
(ii) Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression, is an indication that the patient is at risk of suffering from preeclampsia.
The term “risk of suffering from a disease”, in particular preeclampsia, as used herein, refers to a likelihood or probability that a subject develops a clinical condition within a defined time interval. As used herein, the term “determining the risk of suffering from a disease”, in particular, preeclampsia is understood as the assessment of the future clinical status of a patient prior to any sign of disease or symptom in said patient. If a risk of suffering a disease is
determined, the clinical status of the patient is likely to change into a status of clinical symptomatology within a given time period after the assessment of the future status of said patient. If the patient is determined not to be at risk of suffering a disease, the clinical status of the patient is likely not to change into a status of clinical symptomatology within a given time period after the assessment of the future status of said patient is determined.
As used herein, the term “suffering from preeclampsia” refers to the appearance of any symptoms or clinical condition related to the placental insufficiency syndrome. Suitable criteria for determining whether a subject suffers from preeclampsia can be found, for instance, in the clinical manual such as Poon LC et al. (The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention. Int J Gynaecol Obstet. 2019 May;145 Suppl l(Suppl 1): 1-33. doi: 10.1002/ijgo.12802. Erratum in: Int J Gynaecol Obstet. 2019 Sep;146(3):390-391. PMID: 31111484; PMCID: PMC6944283) and ACOG Practice Bulletin No. 202: Gestational Hypertension and Preeclampsia. Obstet Gynecol. 2019 Jan; 133(1): 1. doi: 10.1097/AOG.0000000000003018. PMID: 30575675.
The term “suffering from preeclampsia” may also refer to any accompanying clinical signs of preeclampsia including but not limited to hypertension, proteinuria, maternal vascular damage, elevated blood pressure (systolic >160 or diastolic of >100 mm Hg) or thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema, cerebral or visual disturbances, and combinations thereof.
As used herein, the patient is at risk of suffering from preeclampsia with a given sensitivity and specificity if the levels of expression of the at least one gene according to the invention in the sample of the patient deviates from a pre-established threshold level of expression.
As used herein, “determining of the risk of suffering from preeclampsia” in a patient is based on the measurement of the level of expression of at least one gene according to the invention in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle. Accordingly, the method of the invention allows the determination of the risk of suffering preeclampsia before the start of pregnancy.
In a first step, the method comprises measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, in Table 3 or in Table 12 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
The term "expression level”, as used herein, refers to the measurable quantity of gene product produced by the gene in a sample of the subject, wherein the gene product can be a transcriptional product or a translational product. As understood by the person skilled in the art, the gene expression level can be quantified by measuring the messenger RNA levels of said gene or of the protein encoded by said gene. In the context of the present invention, the expression level of the genes used in the method according to the invention can be determined by measuring the levels of mRNA encoded by said gene, or by measuring the levels of the protein encoded by said gene, i.e. the protein or variants thereof. Variants of the proteins encoded by the genes which are measured according to the method of the invention include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained.
The term “sample” or “biological sample”, as used herein, refers to biological material isolated from a subject. The biological sample contains any biological material suitable for detecting DNA, RNA or protein levels. In a particular embodiment, the sample comprises genetic material, e.g., DNA, genomic DNA (gDNA), complementary DNA (cDNA), RNA, heterogeneous nuclear RNA (hnRNA), mRNA, etc., from the subject under study. The sample can be isolated from any suitable tissue or biological fluid such as, for example blood, saliva, plasma, serum, urine, cerebrospinal liquid (CSF), feces, a surgical specimen, a specimen obtained from a biopsy, and a tissue sample embedded in paraffin. In a particular embodiment, the sample from the subject according to the methods of the present invention is an endometrial tissue sample.
Gene expression levels can be quantified by measuring the messenger RNA levels of the gene or of the protein encoded by said gene or of the protein encoded by said gene or of variants thereof. Protein variants include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation,
etc., provided that the functionality of the protein is maintained. Said term encompasses the protein of any mammal species, including but not being limited to domestic and farm animals (cows, horses, pigs, sheep, goats, dogs, cats or rodents), primates and humans. Preferably, the protein is a human protein.
In order to measure the levels of the mRNA encoded by a given gene, the biological sample may be treated to physically, mechanically or chemically disrupt tissue or cell structure, to release intracellular components into an aqueous or organic solution to prepare nucleic acids for further analysis. The nucleic acids are extracted from the sample by procedures known to the skilled person and commercially available. RNA is then extracted from frozen or fresh samples by any of the methods typical in the art, for example, Sambrook, J., et ah, 2001. Molecular cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, N. Y., Vol. 1-3. In some embodiments, the RNA is extracted from formalin-fixed, paraffin embedded tissues. An exemplary deparaffmization method involves washing the paraffmized sample with an organic solvent, such as xylene, for example. Deparaffmized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example include, methanol, ethanol, propanols, and butanols. Deparaffmized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example. Alternatively, the sample is simultaneously deparaffmised and rehydrated. The sample is then lysed and RNA is extracted from the sample. Commercially available kits may be used for RNA extraction from paraffin samples, such as PureLink™ FFPE Total RNA Isolation Kit (Thermofisher Scientific Inc., US). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (1987) Lab Invest. 56:A67, and De Andres et ak, BioTechniques 18:42044 (1995). Preferably, care is taken to avoid degradation of the RNA during the extraction process.
Various technologies are well-known in the art for deducing and/or measuring and/or detecting the levels of one or more transcripts in a cell. Such methods include hybridization-or sequence- based approaches. Hybridization-based approaches typically involve incubating fluorescently labelled cDNA with custom-made microarrays or commercial high-density oligo microarrays. Specialized microarrays have also been designed; for example, arrays with probes spanning exon junctions can be used to detect and quantify distinct spliced isoforms. Genomic tiling microarrays that represent the genome at high density have been constructed and allow the
mapping of transcribed regions to a very high resolution, from several base pairs to -100 bp. Hybridization-based approaches are high throughput and relatively inexpensive, except for high-resolution tiling arrays that interrogate large genomes. However, these methods have several limitations, which include: reliance upon existing knowledge about genome sequence; high background levels owing to cross-hybridization; and a limited dynamic range of detection owing to both background and saturation of signals. Moreover, comparing expression levels across different experiments is often difficult and can require complicated normalization methods.
In contrast to microarray methods, sequence-based approaches directly determine the cDNA sequence. Initially, Sanger sequencing of cDNA or EST libraries was used, but this approach is relatively low throughput, expensive and generally not quantitative. Tag-based methods were developed to overcome these limitations, including serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequencing (MPSS). These tag-based sequencing approaches are high throughput and can provide precise, digital gene expression levels. However, most are based on Sanger sequencing technology, and a significant portion of the short tags cannot be uniquely mapped to the reference genome. Moreover, only a portion of the transcript is analyzed, and isoforms are generally indistinguishable from each other. These disadvantages limit the use of traditional sequencing technology in measuring or detection mRNA levels.
The present methods can also involve a larger-scale analysis of mRNA levels, e.g., the detection of a plurality of biomarkers (e.g., 2-10, or 5-50, or 10-100, or 50-500 or more at one time). In addition, the methods described here can also involve the step of conducting a transcriptomic analysis (i.e., the analysis of the complete set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition). Understanding the transcriptome can be important for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and disease and how the biomarkers disclosed herein are indicative or predictive of a particular condition (e.g., LM or LMS). The key aims of transcriptomics are: to catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs; to determine the transcriptional structure of genes, in terms of their start sites, 5' and 3' ends, splicing patterns
and other post-transcriptional modifications; and to quantify the changing expression levels of each transcript during development and under different conditions.
Recently, the development of novel high-throughput DNA sequencing methods has provided a new method for both mapping and quantifying transcriptomes. This method, termed RNA- Seq (RNA sequencing), has advantages over existing approaches for determining transcriptomes. Accordingly, in one embodiment, the expression level of the gene or genes used in the first method of the invention are determined by RNAseq.
As used herein "RNAseq" or"RNA-seq" is used to refer to a transcriptomic approach where the total complement of RNAs from a given sample is isolated and sequenced using high- throughput next generation sequencing (NGS) technologies (e.g., SOLiD, 454, Illumina, or ION Torrent).
RNA-Seq uses deep-sequencing technologies. In general, a population of RNA (total or fractionated, such as poly(A)+) is converted to a library of cDNA fragments with adaptors attached to one or both ends. Each molecule, with or without amplification, is then sequenced in a high-throughput manner to obtain short sequences from one end (single-end sequencing) or both ends (pair-end sequencing). The reads are typically 30-400 bp, depending on the DNA- sequencing technology used. In principle, any high-throughput sequencing technology can be used for RNA-Seq, e.g., the Illumina IG18, Applied Biosystems SOLiD22 and Roche 454 Life Science systems have already been applied for this purpose. The Helicos Biosciences tSMS system is also appropriate and has the added advantage of avoiding amplification of target cDNA. Following sequencing, the resulting reads are either aligned to a reference genome or reference transcripts, or assembled de novo without the genomic sequence to produce a genome-scale transcription map that consists of both the transcriptional structure and/or level of expression for each gene.
Transcriptome analysis by next-generation sequencing (RNA-seq) allows investigation of a transcriptome at unsurpassed resolution. One major benefit is that RNA-seq is independent of a priori knowledge on the sequence under investigation.
The transcriptome can be profiled by high throughput techniques including SAGE, microarray, and sequencing of clones from cDNA libraries. For more than a decade, oligo nucleotide microarrays have been the method of choice providing high throughput and affordable costs. However, microarray technology suffers from well- known limitations including insufficient sensitivity for quantifying lower abundant transcripts, narrow dynamic range and biases arising from non-specific hybridizations. Additionally, microarrays are limited to only measuring known/annotated transcripts and often suffer from inaccurate annotations. Sequencing -based methods such as SAGE rely upon cloning and sequencing cDNA fragments. This approach allows quantification of mRNA abundance by counting the number of times cDNA fragments from a corresponding transcript are represented in a given sample, assuming that cDNA fragments sequenced contain sufficient information to identify a transcript. Sequencing-based approaches have a number of significant technical advantages over hybridization- based microarray methods. The output from sequence-based protocols is digital, rather than analog, obviating the need for complex algorithms for data normalization and summarization while allowing for more precise quantification and greater ease of comparison between results obtained from different samples. Consequently, the dynamic range is essentially infinite, if one accumulates enough sequence tags. Sequence-based approaches do not require prior knowledge of the transcriptome and are therefore useful for discovery and annotation of novel transcripts as well as for analysis of poorly annotated genomes. However, until recently the application of sequencing technology in transcriptome profiling has been limited by high cost, by the need to amplify DNA through bacterial cloning, and by the traditional Sanger approach of sequencing by chain termination.
The next-generation sequencing (NGS) technology eliminates some of these barriers, enabling massive parallel sequencing at a high but reasonable cost for small studies. The technology essentially reduces the transcriptome to a series of randomly fragmented segments of a few hundred nucleotides in length. These molecules are amplified by a process that retains spatial clustering of the PCR products, and individual clusters are sequenced in parallel by one of several technologies. Current NGS platforms include the Roche 454 Genome Sequencer, Illumina's Genome Analyzer, and Applied Biosystems' SOLiD. These platforms can analyze tens to hundreds of millions of DNA fragments simultaneously, generate giga-bases of sequence information from a single run, and have revolutionized SAGE and cDNA sequencing technology. For example, the 3' tag Digital Gene Expression (DGE) uses oligo-dT priming for
first strand cDNA synthesis, generates libraries that are enriched in the 3' untranslated regions of polyadenylated mRNAs, and produces base cDNA tags.
In various embodiments the use of such sequencing technologies does not require the preparation of sequencing libraries. However, in certain embodiments the sequencing methods contemplated herein requires the preparation of sequencing libraries.
Any method for making high-throughput sequencing libraries can be used. An example of sequencing library preparation is described in U.S. Patent Application Publication No. US 2013/0203606, which is incorporated by reference in its entirety. In some embodiments, this preparation may take the coagulated portion of the sample from the droplet actuator as an assay input. The library preparation process is a ligation-based process, which includes four main operations: (a) blunt-ending, (b) phosphorylating, (c) A-tailing, and (d) ligating adaptors. DNA fragments in a droplet are provided to process the sequencing library. In the blunt-ending operation (a), nucleic acid fragments with 5'- and/or 3 '-overhangs are blunt-ended using T4 DNA polymerase that has both a 3 '-5' exonuclease activity and a 5'-3' polymerase activity, removing overhangs and yielding complementary bases at both ends on DNA fragments. In some embodiments, the T4 DNA polymerase may be provided as a droplet. In the phosphorylation operation (b), T4 polynucleotide kinase may be used to attach a phosphate to the 5'-hydroxyl terminus of the blunt-ended nucleic acid. In some embodiments, the T4 polynucleotide kinase may be provided as a droplet. In the A-tailing operation (c), the 3' hydroxyl end of a dATP is attached to the phosphate on the 5 '-hydroxyl terminus of a blunt- ended fragment catalyzed by exo-Klenow polymerase. In the ligating operation (d), sequencing adaptors are ligated to the A-tail. T4 DNA ligase is used to catalyze the formation of a phosphate bond between the A-tail and the adaptor sequence. In some embodiments involving cfDNA, end-repairing (including blunt-ending and phosphorylation) may be skipped because the cfDNA are naturally fragmented, but the overall process upstream and downstream of end repair is otherwise comparable to processes involving longer strands of DNA.
In another example, sequencing library preparation can involve the production of a random collection of adapter-modified DNA fragments (e.g., polynucleotides) that are ready to be sequenced. Sequencing libraries of polynucleotides can be prepared from DNA or RNA, including equivalents, analogs of either DNA or cDNA, for example, DNA or cDNA that is
complementary or copy DNA produced from an RNA template, by the action of reverse transcriptase. The polynucleotides may originate in double-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA, PCR amplification products, and the like) or, in certain embodiments, the polynucleotides may originated in single-stranded form (e.g., ssDNA, RNA, etc.) and have been converted to dsDNA form.
By way of illustration, in certain embodiments, single stranded mRNA molecules may be copied into double-stranded cDNAs suitable for use in preparing a sequencing library. The precise sequence of the primary polynucleotide molecules is generally not material to the method of library preparation, and may be known or unknown. In one embodiment, the polynucleotide molecules are DNA molecules. More particularly, in certain embodiments, the polynucleotide molecules represent the entire genetic complement of an organism or substantially the entire genetic complement of an organism, and are genomic DNA molecules (e.g., cellular DNA, cell free DNA (cfDNA), etc.), that typically include both intron sequence and exon sequence (coding sequence), as well as non-coding regulatory sequences such as promoter and enhancer sequences. In certain embodiments, the primary polynucleotide molecules comprise human genomic DNA molecules, e.g., cfDNA molecules present in peripheral blood of a subject.
Preparation of sequencing libraries for some NGS sequencing platforms is facilitated by the use of polynucleotides comprising a specific range of fragment sizes. Preparation of such libraries typically involves the fragmentation of large polynucleotides (e.g. cellular genomic DNA) to obtain polynucleotides in the desired size range.
In a second step, the method comprises determining whether the patient is at risk of suffering from preeclampsia if the level of expression of the gene measured in the first step deviates with respect to a pre-established threshold level of expression.
A reference gene expression level can be a “threshold” level or a “cut-off’ level. Typically, a “threshold level” or “cut-off level” can be determined experimentally, empirically, or theoretically.
The suitable reference expression levels of the at least one gene according to the invention can be determined by measuring the expression level of said gene in several suitable subjects, and
such reference level can be adjusted to specific subject populations. For example, a reference level can be linked to non-pregnant subjects with a history of preeclampsia so that comparisons can be made between expression levels in samples of non-pregnant subjects and reference levels for preeclampsia.
In a particular embodiment, the reference sample may be a pool of samples of endometrial tissue from several individuals.
In a particular embodiment, the pre-established threshold level of expression corresponding to the at least one gene is determined in a sample from a subject or a pool of subjects which have suffered from preeclampsia.
In another embodiment, the pre-established threshold level of expression of the at least one gene is determined in a sample from a subject or a pool of subjects which have not suffered from preeclampsia.
A “pre-established threshold level of expression”, as used herein, may refer to the level of expression of at least one gene determined in a non-pregnant subject who suffered from preeclampsia in a previous pregnancy or to the expression level in a subject who was tested 1 to 6 days before the end of the menstrual cycle and which did not develop preeclampsia during a subsequent pregnancy.
A “pre-established threshold level of expression”, as used herein, may refer to the level of expression of at least one gene determined in non-pregnant subjects who have not suffered preeclampsia in a previous pregnancy.
A “pre-established threshold level of expression”, as used herein, may also refer to the level of expression of at least one gene determined in non-pregnant healthy subjects.
As used herein, a patient which has no symptoms of preeclampsia, but has a high probability to develop clinical symptoms of preeclampsia as pregnancy proceeds, is a patient at risk of suffering from preeclampsia.
As used herein, a patient which has no symptoms of preeclampsia and has a low probability to develop clinical symptoms of preeclampsia as pregnancy proceeds, is not a patient at risk of suffering from preeclampsia.
The method of the invention allows for a classification of a subject based on the risk of said subject to suffer from preeclampsia to develop clinical signs related to preeclampsia within a defined time interval. As used herein, a defined time interval may refer to the period of pregnancy. According to the invention, the method for predicting the risk of suffering from preeclampsia allows the classification or selection of a pregnant patient as i) being at risk of suffering from preeclampsia or ii) not being at risk of suffering from preeclampsia.
Once the pre-established threshold level of expression corresponding to the at least one gene is established, the levels of expression of the at least one gene in a subject in which the risk of suffering from preeclampsia is to be determined can be compared with the pre-established threshold level, and thus be assigned as “increased”, “decreased” or “equal”.
In some embodiment, the expression profile of the genes in the reference sample can preferably be generated from a population of two or more individuals. The population, for example, can comprise 3, 4, 5, 10, 15, 20, 30, 40, 50 or more individuals.
According to the present invention, a patient may be classified as having the status of being at risk of suffering from preeclampsia based on the deviation of the level of expression of at least one gene with respect to a pre-established threshold level of expression.
According to the present invention, a patient may be classified as having the status of not being at risk of suffering from preeclampsia based on the deviation of the level of expression of at least one gene according to the invention with respect to a pre-established threshold level of expression.
In some embodiments, a deviation in the level of expression of at least one gene according to the invention may refer to an increase in the level of expression as compared with a pre- established threshold level of expression.
In some embodiments, a deviation in the level of expression of at least one gene may refer to a reduction in the level of expression as compared with a pre-established threshold level of expression.
In some embodiments, an increase in the level of expression of at least 1.1-fold, 1.5-fold, 5- fold, 10-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the pre-established threshold level of expression is considered as “increased” level.
In some embodiments, a reduction in the level of expression of at least 1.1-fold, 1.5-fold, 5- fold, 10-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the pre-established threshold level of expression is considered as “reduced” level.
In some embodiments, the term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene may relate to a situation where the level of the at least one gene is increased at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% when compared to the corresponding pre-established threshold level of expression.
In some embodiments, the term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene may relate to a situation where the level of expression of the at least one gene is reduced at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% when compared to the corresponding pre-established threshold level of expression.
Levels can be seen as “equal” to the pre-established threshold level of expression if the levels differ with respect to the pre-established threshold level of expression less than 5%, less than 4%, less than 3%, less than 2%, less than 1%, less than 0.5 %, less than 0.4%, less than 0.3%, less than 0.1%, less than 0.05%, or less.
In a preferred embodiment, the method of the invention is characterized in that the expression level of at least one gene shown in Table 21 is determined.
In a preferred embodiment, the method of the invention is characterized in that the expression level of at least one gene shown in Table 4 is determined.
In some embodiments, the deviation in the level of expression of at least one gene according to the invention may refer to a log2fold change.
The term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene according to the invention may relate to a situation where the level of expression of the at least one gene is increased a log2fold of at least 1, a log2fold of at least 1.5, a log2fold of at least 2, a log2fold of at least 2.5, a log2fold of at least 3, a log2fold of at least 5, compared with a pre-established threshold level of expression is considered as “increased” level.
The term “at risk of suffering from preeclampsia” as used herein in relation to the level of expression of at least one gene according to the invention may relate to a situation where the level of expression of the at least one gene is reduced a log2fold of at least 1, a log2fold of at least 1.5, a log2fold of at least 2, a log2fold of at least 2.5, a log2fold of at least 3, a log2fold of at least 5, compared with a pre-established threshold level of expression is considered as “reduced” level.
In another preferred embodiment, the method of the invention is characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 3, a log2fold change of at least 2 in the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 9 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 10.
In another embodiment, the in vitro method is characterized in that the expression levels of all genes comprised in Table 3, in Table 8, in Table 9 and/or, in Table 10.
In another embodiment, the in vitro method is characterized in that the expression levels of all genes comprised in Table 13.
In another preferred embodiment, the method of the invention is characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 15, a log2fold change of at least 2 in the expression of at least a gene included in Table 16, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 17 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 18.
In another embodiment, the in vitro method is characterized in that the expression levels of all genes comprised in Table 15, in Table 16, in Table 17, in Table 18, in Table 19.
Method for determining the risk of suffering preeclampsia based on the expression levels of the progesterone receptor or of the estrogen receptor 1
In another aspect, the invention relates to an in vitro method for determining the risk of suffering from preeclampsia comprises:
(i) Measuring the expression level of the progesterone receptor or of the estrogen receptor 1 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and
(ii) Wherein reduced expression levels of expression of the progesterone receptor or of the estrogen receptor 1 in step (i) as compared with a reference expression value, is an indication that the patient is at risk of suffering from preeclampsia.
The terms “determining the risk of suffering from preeclampsia”, “reference value”, “reduced expression”, “biological sample” have been described in detail in respect to the previous method of the invention and are equally applicable to the present method.
The term “progesterone receptor” as used herein, refers to mRNA encoding the isoform B of the progesterone receptor gene and which results from the alternative splicing of the transcript encoded by the progesterone receptor gene which is shown in the HGNC database under
accession number 8910, in the NCBI Entrez Gene database under accession number 5241, in the Ensembl database under accession number ENSG00000082175, in the OMIM® database under accession number 607311. The progesterone receptor isoform B polypeptide is shown in the UniProtKB/Swiss-Prot database under accession number P06401-1.
The term “estrogen receptor 1”, as used herein, refers to the gene encoding a nuclear hormone and which is shown in the HGNC database under accession number 3467, in the NCBI database under Entrez Gene number 2099, in the Ensembl database under accession number ENSG00000091831, in the OMIM® database under accession number 133430 and which encodes a polypeptide shown in the UniProtKB/Swiss-Prot database under accession number P03372.
Suitable methods for determining the expression levels of the progesterone receptor or of the estrogen receptor 1 have been described in detail in the previous aspects of the invention and are equally applicable to the present method. In one embodiment, the biological sample is an endometrial tissue sample.
In vitro method for the prognosis of preeclampsia
As used herein, the term “prognosis of preeclampsia” may be understood as the prospect of recovery as anticipated from the usual course of preeclampsia or peculiarities of the case. Also, the term “prognosis” may refer to the likely outcome or course of a disease; the chance of recovery or recurrence. Accordingly, the terms “prognosis of preeclampsia” and “risk of suffering from preeclampsia” as used herein may be understood as synonyms.
In another aspect, the present invention relates to an in vitro method for the prognosis of preeclampsia.
In a first step the method comprises measuring the expression level of at least a gene selected from Table 3, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
In a second step, the method comprises determining whether the patient is suffering from preeclampsia or has a bad prognosis if the level of expression of the gene measured in the first step deviates with respect to a pre-established threshold level of expression.
In a preferred embodiment, the In vitro method, according to claim 1, characterized in that step
(i) comprises the measurement of the expression level of at least a gene selected from Table 4, or any combination thereof.
Computer implemented methods of the invention
In another aspect, the invention relates to any of the methods defined in the previous aspects in the invention in which the method is computer implemented. The method comprises:
(i) Entering into the computer the reference expression levels of the genes which are measured in step (i);
(ii) Entering into the computer the level of expression of the genes obtained in the step (i) of the previous claims;
(iii) Producing a score which is displayed on the device;
(iv) Determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with the reference expression levels.
Methods of the invention can be performed using software, hardware, firmware, hardwiring, or combinations of any of these.
Methods for the prediction of the risk of suffering from preeclampsia in a subject and subsequent treatment of the subject
In another aspect, the invention provides a method wherein a patient is selected based on showing an increased risk of suffering preeclampsia, said patient is then monitored in any subsequent pregnancy for the appearance of the symptoms of preeclampsia and then, if preeclampsia is detected, the patient is treated with a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia.
Thus, in another aspect, the invention relates to a method for predicting the risk of suffering preeclampsia and treating preeclampsia in a subject which comprises:
(i) Determining the risk of suffering from preeclampsia in said subject by a method according to any of methods for predicting the risk of suffering preeclampsia according to the present invention,
(ii) Monitoring the subjects which have been determined in step (i) to be at high risk of suffering preeclampsia in order to detect the appearance of preeclampsia and
(iii) Administering to said subject a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once preeclampsia has been detected.
In another aspect, the invention relates to a therapy useful in the treatment of preeclampsia for use in the treatment of a subject suffering from preeclampsia, wherein the patient was identified as having high risk of suffering preeclampsia by any of the methods according to the invention and then further selected by detecting the appearance of preeclampsia during a pregnancy subsequent to the prediction of the risk of suffering preeclampsia.
The appearance of preeclampsia can be detected in the patients identified as having high risk of suffering preeclampsia by detecting of any of the following:
• severe headaches.
• vision problems, such as blurring or seeing flashing lights.
• severe heartburn.
• pain just below the ribs.
• nausea or vomiting.
• excessive weight gain caused by fluid retention.
• feeling very unwell.
• sudden increase in oedema - swelling of the feet, ankles, face and hands.
Suitable treatments which could be used for treating preeclampsia include:
• Medications to lower blood pressure. These medications, called antihypertensives, are used to lower your blood pressure if it's dangerously high. Blood pressure in the 140/90 millimetres of mercury (mm Hg) range generally isn't treated.
• Corticosteroids. If you have severe preeclampsia or HELLP syndrome, corticosteroid medications can temporarily improve liver and platelet function to help prolong your pregnancy. Corticosteroids can also help your baby's lungs become more mature in as little as 48 hours, an important step in preparing a premature baby for life outside the womb.
• Anticonvulsant medications. If your preeclampsia is severe, your doctor may prescribe an anticonvulsant medication, such as magnesium sulphate, to prevent a first seizure.
Kit of the invention and uses thereof
In another aspect, the invention relates to a kit, package or device that contains reagents adequate for implementing any of the methods of the invention. It will be understood that, depending on the nature of the method, the reagents adequate for its implementation will vary.
In the context of the present invention, “kit” is understood as a product containing the different reagents required for carrying out the methods of the invention packaged such that it allows being transported and stored. The materials suitable for the packaging of the components of the kit include glass, plastic (polyethylene, polypropylene, polycarbonate, and the like), bottles, vials, paper, sachets, and the like. Where there are more than one component in a kit they may be packaged together if suitable or the kit will generally contain a second, third or other additional container into which the additional components may be separately placed. However, in some embodiments, certain combinations of components may be packaged together comprised in one container means. A kit can also include a means for containing any reagent containers in close confinement for commercial sale. Such containers may include injection or blow-molded plastic containers into which the desired vials are retained. One or more compositions of a kit can be lyophilized. In some embodiments, all compositions of a kit of the disclosure will be lyophilized. In some embodiments, a kit of the disclosure with one or more lyophilized agents will be supplied with a re-constitution buffer. Reagents and components of kits may be comprised in one or more suitable container means. A container means may generally comprise at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted.
Furthermore, kits according to the invention can also comprise one or more reagents for
preparing crude cell lysates and/or reagents for extracting, isolating and/or purification of nucleic acids from a sample. Additional components can comprise particles with affinity for nucleic acids and/or solid supports with affinity for nucleic acids, one or more wash buffers, binding enhancers, binding solutions, polar solvents, alcohols, elution buffers, filter membranes and/or columns for isolation of DNA/RNA. A kit may further comprise reagents for downstream processing of an isolated nucleic acid and may include without limitation at least one RNase inhibitor; at least one cDNA construction reagents (such as reverse transcriptase); one or more reagents for amplification of RNA, one or more reagents for amplification of DNA including primers, reagents for purification of DNA, probes for detection of specific nucleic acids. Furthermore, the kits of the invention can contain instructions for the simultaneous, sequential, or separate use of the different components that are in the kit. Said instructions can be in the form of printed material or in the form of an electronic medium capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes, and the like), optical media (CD-ROM, DVD), and the like. The media may additionally or alternatively contain Internet addresses providing said instructions.
In some embodiments, the kit comprises primers or probes adequate for the detection of the expression levels of one or more of the genes, the expression levels of which are determined in the any of the methods according to the invention.
The term "primer" as used herein refers to oligonucleotides that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the primer within a sequence of the target polynucleotide sequence. A primer can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides. For proper hybridization to the target sequence, a primer can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to the hybridized portion of the target polynucleotide sequence. Oligonucleotides useful as primers may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts. (1981) 22: 1859-1862, using an automated synthesizer, as described in Needham-Van Devanter et al, Nucleic Acids Res. (1984) 12: 6159-6168. Primers are useful in nucleic acid amplification reactions in which the primer is extended to produce a new strand of the polynucleotide. Primers can be readily designed by a skilled artisan using common knowledge known in the art, such that they can
specifically anneal to the nucleotide sequence of the target nucleotide sequence of the at least one biomarker provided herein. Usually, the 3' nucleotide of the primer is designed to be complementary to the target sequence at the corresponding nucleotide position, to provide optimal primer extension by a polymerase.
The term "probe" as used herein refers to oligonucleotides or analogs thereof that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the probe within a sequence of the target polynucleotide sequence. Exemplary probes can be, for example DNA probes, RNA probes, or protein nucleic acid (PNA) probes. A probe can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides. For proper hybridization to the target sequence, a probe can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to hybridized portion of the target polynucleotide sequence. Probes can also be chemically synthesized according to the solid phase phosphoramidite triester method as described above. Methods for preparation of DNA and RNA probes, and the conditions for hybridization thereof to target nucleotide sequences, are described in Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition. Cold Spring Harbor Laboratory Press, 1989, Chapters 10 and 11.
In a preferred embodiment, the reagents adequate for the determination of the expression levels of one or more genes comprise at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% of the total amount of reagents adequate for the determination of the expression levels of genes forming the kit.
The present invention is illustrated by means of the Examples below without the intention of limiting its scope of protection.
Example 1. Material and methods Example 1.1. Study Design
A total of 40 non-pregnant women that experienced a previous pregnancy were enrolled in this study for endometrial RNA sequencing analysis. Endometrial samples were obtained during late secretory phase in 24 women that have developed sPE in a previous pregnancy and 16 with no history of sPE with term (n=8) and preterm pregnancies (n=8) previous as controls.
Endometrial biopsies were processed to obtain RNA and then converted to cDNA for library generation to perform next generation sequencing (NGS). The experimental design was based on a stratified random sampling with a 70:30 proportion in two cohorts: training (n=29) and validation (n=l 1) set of samples. Training set of samples was analyzed by RNA sequencing to identify the global transcriptomic profiling changes between control (n=12) and sPE (n=17) patients. Selection criteria were applied to define a transcriptomic fingerprinting associated to decidualization defect detected in sPE. Finally, a targeted analysis of the fingerprinting DD signature was validated in the test set composed by controls (n=4) and sPE (n=7).
Example 1.2. Endometrial Sample Collection
Samples were collected from women aged 18-42 without any medical condition who had been pregnant 1-8 years earlier. All patients had regular menstrual cycle (26-32 days), with no underlying gynecological pathologic conditions, and had not received hormonal therapy in the 3 months preceding sample collection. After the inclusion criteria were applied, endometrial biopsies were obtained by pipelle (Genetics Hamont-Achel, Belgium) under sterile conditions in the late secretory (LS) phase (cycle days 22-32). Specimens were kept in preservation solution until processing.
This study was approved by the Clinical Research Ethics Committee of Hospital La Fe (Valencia, Spain) (2011/0383), and written informed consent was obtained from all patients before tissue collection, and all samples were anonymized.
Example 1.3. RNA extraction
Total RNA from endometrial biopsies was isolated using QIAsymphony RNA kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. RNA concentrations were quantified using a Multiskan GO spectrophotometer (Thermo Fisher Scientific, Waltham, CIS) at a wavelength of 260 nm. Integrity of the total RNA samples was evaluated by the RNA integrity number (RIN) and DV200 metrics using an Agilent high-sensitivity RNA ScreenTape in a 4200 TapeStation system (Agilent Technologies Inc., Santa Clara, CA). Samples used for the global RNA-seq showed RIN values ranging from 4.9 to 9.2.
Example 1.4.Global RNAseq library preparation and transcriptome sequencing
The cDNA libraries from total RNA samples (n=40) were prepared by an Illumina TruSeq Stranded mRNA sample prep kit (Illumina, San Diego, CA). Three micrograms of total RNA were used as the RNA input according to recommendations of the manufacturer’s protocol. The mRNAs were selected from the total RNAs by purifying the poly-A containing molecules using poly-T oligo attached to magnetic beads. The RNA fragmentation, first and second strand cDNA syntheses, end repair, single ‘A’ base addition, adaptor ligation, and PCR amplification were performed according to the manufacturer’s protocol. The average size of the cDNA libraries was approximately 350 bp (including the adapters). The cDNA libraries were validated for RNA integrity and quantity using an Agilent 4200 TapeStation system (Agilent Technologies Inc., Santa Clara, CA) before pooling the libraries. The pool concentration was quantified by a qPCR using the KAPA Library Quantification Kit (Kapa Biosystems Inc.) before sequencing in a NextSeq 500/550 cartridge of 150 cycles (Illumina, San Diego, CA). Indexed and pooled samples were sequenced 150-bp paired-end reads by on the Illumina NextSeq 500/550 platform, according to Illumina library protocol.
Example 1.5. RNA-seq analysis
Reads were mapped to the hgl9 human genome transcriptome using the STAR (version 2.4.2 a) read aligner (Dobin et al., 2013). FastQC (version 0.11.2) was used to determine the quality of FASTQ files. The manipulation of SAM and BAM files was done with the software SAMtools (version 1.1) (Li et al., 2009). To count the number of reads that could be assigned to each gene, we used HTSeq (version 0.6. lpl; Anders et al., 2015) and BEDtools software (version 2.17.0; Quinlan and Hall, 2010) to obtain gene coverage and work with bedFiles. Quality control filters in each program were used following the software package recommendations, and reads were filtered by mapping quality greater than 90%. Transcriptomic data were deposited in the Gene Expression Omnibus database (accession number GSE172381). The Bioconductor package edgeR (version 3.24.3; Robinson etak, 2010) was used to analyze differentially expressed genes (DEGs). The trimmed mean of M-values normalization method was applied to our gene expression values. Differentially expressed genes were obtained through two approaches. In the first approach, the exactTest function was used and the p-value adjustment method was false discovery rate (FDR) with a cutoff of 0.05 (FDR < 0.05). Once the p-value was adjusted, significant deregulated genes with log2-fold- change > 1 (FC >2) were selected. Differentially expressed genes were obtained through two approaches. In the second approach, the glmTreat function was used to find DEGs between
groups. The p-value adjustment method was false discovery rate (FDR) with a cutoff of 0.05 (FDR < 0.05) and the fold-change (FC) threshold was 1.2. edgeR analysis was carried out in R version 3.5.1. A volcano plot was created to visualize DEGs. Custom scripts are available on GitHub at link https://github.com/mclemente-igenomix/garrido_et_al_2021.
Example 1.6. Transcriptomic fingerprinting definition and validation
In the first approach, genes with assigned EntrezID with an FDR cut-off of 0.05 and an expression >4-fold higher in the sPE vs. control training set samples were selected to define a fingerprint associated with DD in sPE. In the second approach, genes with assigned EntrezID with an FDR cutoff of 0.05 and an expression >1.4- fold higher in the sPE vs. control training set samples were selected to define a fingerprint associated with DD in sPE. Targeted analysis of fingerprinting genes was performed using the validation set of samples. PCA and unsupervised hierarchical clustering with a Canberra distance based on gene signature were performed comparing sPE to control specimens. Custom scripts are available on GitHub at https:// github.com/mclemente-igenomix/garrido_et_al_2021.
Example 1.7. Enrichment Analysis
Biological process in which those differentially expressed genes (DEGs) are involved were studied. In edgeR, GO analyses can be conducted using the goana. In the first approach, an FDR cutoff of 5% is used when extracting DE genes and for log2FC we use cut-off value of 1 [UP, log2FC>l and DOWN; log2FC <(-1)]. The ontology domain that GO term belongs to is biological process (BP). As the p-values obtained are not adjusted for multiple testing, we would ignore GO terms with p-values greater than about 0.005. . In the second approach, the input genes were those 120 included in the fingerprinting. The p-value adjustment method was FDR with a cut-off of 0.05 (FDR<0.05). Custom scripts are available on GitHub at https://github.com/mclemente-igenomix/garrido_et_al_2021.
Example 1.8. qRT-PCR Gene Validation.
To validate our transcriptomic results a selection of differentially expressed genes was validated by qRT-PCR in a subgroup of samples from the experimental cohort [controls (n=9) and sPE (n= 14)] . Specific primers for each gene were described in Table 1 (RT-qPCR primers list). The primers included in Table 1 correspond with SEQ ID NO: 1 to 22.
Table 1
Sequence Name Sequence
AOX1 FW TGTCCATCTACACGCTGCTC
AOX1_RV TCCTCAAATTCTGGCAATCC
ERP27_FW ACAAGGCCTCCCCAGAGTAT
ERP27_RV CTTCTGCTGTGGGCAGTGTA
ISM1_FW GACCTGTGACCGTCCAAACT
ISM1_RV AGAACTCGCTTTTGCAGCTC
MEST_FW CGCAGGATCAACCTTCTTTC
MEST_RV CATCAGTCGTGTGAGGATGG
MFAP2_FW CCAGATCGACAACCCAGACT FAP2_RV GCAAGGCCTGTGTATGGAGT
MMP11_FW GGTCTCTGAGGGTCAAGCAG
MMP11_RV AGTTCATGAGCTGCAACACG
PGRMC1_FW CCTCTGCATCTTCCTGCTCT
PGRMC1_RV CGTTGATGGCCATGAGTATG
PGR_FW GTGGGAGCTGTAAGGTCTTCTTTAA
PGR_RV AACGATGCAGTCATTTCTTCCA
PGRB_FW TCGGACACCTTGCCTGAAGT
PGRB_RV CAGGGCCGAGGGAAGAGTAG
REEP2_FW GGGTGCTGTCAGAGAAGCTC
REEP2_RV TGTCTCCCATGTCATCCTCA
WNT5A_FW TGGCTTTGGCCATATTTTTC
WNT5A RV CCGATGTACTGCATGTGGTC cDNA was generated from 400 ng of RNA using the Superscript VILO cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, US). The template cDNA was diluted 5 in 20 and 1 pL was used in each PCR reaction. Real-time PCR was performed in duplicate in 10 pL using commercially validated Kapa SYBR fast qPCR kit (Kapa biosystems Inc, Basilea, Switzerland) and the Lightcycler 480 (Roche Molecular Systems, Inc, Pleasanton, CA) detection system. Samples were run in duplicate along with appropriate controls (i.e. no template, no RT). Cycling conditions were as follows: 95°C for 3 min, 40 cycles of 95°C for 10 s, 60°C for 20 s and 72°C for 1 s. A melting curve was done following the product specifications. Data were analyzed using the comparative Ct method (2-AACT). Data were normalized to the housekeeping gene b-actin, changes in gene expression were calculated using the AACT method with the control group used as the calibrator; values are illustrated relative to median in the control group. Immunofluorescence of tissue sections
Endometrial tissue samples were fixed in 4% paraformaldehyde and preserved in paraffin- embedded blocks. For immunostaining, tissue sections were deparaffinated and rehydrated. Antigen retrieval was performed with buffer citrate 1 x at 100 °C for 10 min. Then, non-specific reactivity was blocked by incubation in 5% BSA/0.1% PBS-Tween 20 at room temperature for 30 min. Sections were incu- bated at room temperature for 1.5 hr with primary antibodies (1:50 rabbit monoclonal anti-human progesterone receptor, Abeam, Cambridge, UK) and 1:50 mouse monoclonal anti-human estrogen receptor 1 (Santa Cruz Biotechnology, CA) diluted in 3% BSA/0.1% PBS-Tween 20. Then, slides were washed two times for 10 min with 0.1% PBS-Tween 20 before they were incubated for 1 hr at room temperature with AlexaFluor- conjugated secondary antibodies diluted in 3% BSA/0.1% PBS- Tween 20 (1:1000). Finally, slides were washed two times in 0.1% PBS-Tween 20. To visualize nuclei, 4',6-diamidino-2- phenylindole at 400 ng/pL was used. Tissue sections were examined using a EVOS M5000 microscope.
Example 1.10. Statistical Analysis
Clinical data are expressed as mean ± standard error mean (SEM). Clinical data were evaluated by two statistical methods: Student's t-test (Table 2) and Wilcoxon test (Table 11) for comparisons between sPE and control samples. Statistical significance was set at P-value of <0.05. Differential expression analysis was performed using the R package edgeR.
Example 2. First analysis of the global transcriptional signature of defective decidualization in vivo from sPE patients
Example 2.1. Global transcriptional signature of defective decidualization in vivo from sPE patients
We assessed the decidualization transcriptional status in vivo of endometrial biopsies obtained in the late secretory (LS) phase from patients that developed sPE in a previous pregnancy (n=24) and controls without sPE (n=16). Controls include patients who had preterm birth with no signs of infection (n=8), and those at term labor with normal obstetric outcomes (n=8). The maternal and neonatal characteristics of the participants are summarized in Table 2.
Table 2. Maternal and neonatal characteristics for endometrial donors
To rule out the existence of global transcriptomic differences based on gestational age at delivery an analysis was conducted comparing preterm and term control specimens. Principal component analysis (PCA) based on transcriptomic profile demonstrated absence of clustering of samples based on their gestational age (Figure 6A). Volcano plot showed zero differentially expressed genes (DEGs) between preterm and term control samples (Figure 6B). Together, these results point toward that the gestational age at delivery is an inert variable at the endometrial transcriptional level.
Our experimental design used a randomly split of samples into two cohorts, training (70%) and test (30%) set (Figure 1A). The random sampling occurs within each class (control and sPE), so the overall class distribution of the data was preserved. The training set (n=29) was used for biomarker identification to obtain a molecular fingerprinting encoding the defective decidualization in sPE, whereas the test set (n=l 1) was used for independent confirmation of our findings. All samples in both cohorts were processed and RNA-sequencing performed in the same manner.
GlobalRNAseq analysis was performed comparing gene expression paterns of sPE (n=17) and controls (n=12) from the training set. After quality trimming and filtering, the reads were aligned to the reference genome hgl9. The raw sequencing genes among the 29 samples were 56,638 and after normalization the number of genes included in the analysis was 18,301. Transcriptional analysis revealed 859 differential expressed genes (DEGs) between sPE and controls (FDR <0.05 and FC>2). Volcano plot showed the yellow dots denoting those statistically significant DEGs with at least two-fold (Figure IB). Specifically, a total of 262 up-regulated and 597 down-regulated genes were identified (Figure 1C). Table 3 is a complete list showing differentially expressed genes which were obtained in the Global RNAseq analysis (859 DEGs). FDR (False Discovery Rate), logFC (logarithim fold change), FC (fold change).
Genes identified mostly include down-regulated mRNAs encoding genes involved in decidualization, such as MMP3, PRL , II -I /, IHH, and SGK1, genes associated to signaling
(e.g. NR4A3 and IL8 ), response to growth factor (e.g. FGF1 and FGF14), angiogenesis (e.g. EDN2 and TMEM215) and immune response ( CCL20 , CXCL3, and IGHG1 ), among others. The up-regulated category identifies molecules involved in amino acid metabolic/catabolic processes ( GGT3P , 11)02, and PRODH ), transport and oxidoreductase activity. To validate dysregulated expression of specific genes identified in the global RNA-seq analysis, we assayed a subgroup of samples from the experimental cohort [controls (n=9) and sPE (n=14)]. Nine relevant genes were selected to validate their expression patterns in sPE vs. control groups (Figure 7A) by RT-qPCR. Fold changes were highly concordant with the sequencing data, corroborating our results (Figure 7B).
Example 2.2. In vivo vs in vitro decidualization fingerprinting
Previously, we reported a decidualization defect detected in endometrial stromal cells (hESCs) isolated from patients with a previous sPE as compared to patients with normal obstetric outcome, but this finding was restricted to the analysis of the endometrial stromal cells (hESCs) using an in vitro decidualization cell culture model. Now, we compared the overlapping between DEGs reported during in vitro (n=129) versus in vivo (n=859) decidualization in sPE compared with control patients. Eighteen genes differentially expressed between the two groups (FDR<0.05 and FC>2) overlapped in both approaches (Figure 2A). They include genes known to be involved during in vitro decidualization, some of them were up-regulated [e.g. ERP27, CHODL , and PRUNE2], and others down-regulated [e.g. ISM 1, MEST,MFAP2 , and REEP2 ]. The expression pattern of the common genes using a sequencing data is presented as a box plot using counts per million corroborating significant differential expression between sPE vs. control (Figure 2B). This result was confirmed by RT-qPCR (Figure 2C) and high correlation (R=0.99) was observed with the global RNAseq results (Figure 2D). However, our current in vivo approach confirms our previous in vitro finding and revealed a broad spectrum of deregulated transcripts.
Example 2.3. Identification of molecular fingerprinting encoding in vivo decidualization defect in sPE patients
To formulate the transcriptomic signature that encodes the in vivo defective decidualization (DD) detected in sPE, we selected those genes with significant deregulation (FDR<0.05), at least four-fold increase (FC> 4) between control and sPE with assigned EntrezID. Volcano plot
showed a total of 166 DEGs meeting these criteria and were included in the final DD- fmgerprinting (Figure 3A). Table 4 is a complete list of genes selected as fingerprinting of defective decidualization (166 DEGs). FDR (False Discovery Rate), logFC (logarithrm fold change), FC (fold change).
Table 4
Interestingly, the number of down-regulated genes was higher compared with the up-regulated genes in sPE vs controls. This observation points toward that in vivo decidualization defect found in sPE are mainly mediated by lack of expression of a subset of genes.
Example 2.4.Functional analysis revealed numerous pathways deregulated in sPE
Gene ontology (GO) analysis of the gene signature associated to defective decidualization in sPE was performed identifying 479 enriched biological process (P -value < 0.005). The down- regulated biological processes highlighted were pathways associated mainly with basic functions such as immune response, signaling, extracellular matrix, cell motility, blood pressure regulation and reproductive process (Figure 3B). All of them are hallmarks of improper decidualization and sPE pathogenesis. More deeply, we found fingerprinting genes representative of the altered pathways in sPE such as, IL6 and TNF regulating the acute inflammatory response, MMP3 and MMP1 participating in the extracellular matrix disassembly, POSTN and REN as affected regulators of the systemic arterial blood pressure and PRL, IHH and ICAM1 implicated in the down-regulated response to hormone (Figure 3C). Up-regulated biological processes were associated with metabolism process, nervous system, interaction between organisms, and negative regulation of chorionic trophoblast cell proliferation. Functional analysis evidenced that the 166 DEGs between sPE vs. control pregnancies included in the fingerprinting were implicated in pathways related with decidualization corroborating the maternal contribution to sPE.
Example 2.5. Validation of the DD-fingerprinting to segregate sPE and control samples
Based on the 166 genes included in the DD-fingerprinting, principal component analysis (PCA) revealed that sPE and control samples clustered separately in two groups, except for three control samples (C20, C21 and C22) (Figure 4A). High variance between both groups was effectively captured in the first 2 principal components. Unsupervised hierarchical clustering analysis confirmed that the gene fingerprinting effectively segregated the two groups; one was composed mainly by controls and the other by sPE samples (Figure 4B). The same three controls were found clustered with the sPE group recreating the PCA results.
We next sought to validate the DD gene signature in an independent cohort of samples [sPE (n=7) vs. control (n=4)] to confirm our findings. Principal component analysis based on these transcripts effectively segregated samples in two homogeneous groups (Figure 4C) that was
corroborated by hierarchical clustering (Figure 4D). These genes successfully grouped 100% of controls and 85.7% of sPE cases. Our results support the relevance of the DD-fmgerprinting as potential preconceptional biomarker of sPE.
Example 2.6. Down expression of progesterone receptor is highly connected with DD- fingerprinting genes in sPE
Progesterone, acting through its receptor (PR), is the key hormone modulating the decidualization. Interestingly, the fingerprinting that undergoes the defective decidualization phenotype of patients with a previous sPE, includes 106 enriched genes in the endometrium tissue defined by the human protein atlas and 50% of them were associated with PR (or PR binding site within EPR, 10 kb or 25 kb)(Figure 5A). Based on this interesting overlapping, we analysed the gene expression of PR in endometrial tissue from a subset of patients with previous sPE (N=13) and control (N=9) pregnancies using quantitative-polymerase chain reaction (Figure 5B). We found significant down-regulation of PR in sPE patients (P -value < 0.001) compared with controls. In order to investigate which PR isoform are responsible of this down-regulation, we determine the expression levels of PR-A and PR-B by quantitative- polymerase chain reaction (Figure 5C). The analysis revealed that PR-B was significantly down-regulated in sPE vs controls (P -value < 0.01), while PR-A was not deregulated (P -value > 0.05). This result corroborates that down-regulation of PR, mediated by altered expression of the isoform PR-B, is associated with sPE.
Functional analysis of the 53 genes from our DD fingerprinting that are modulated by the PR revealed many biological processes including categories related with decidualization biology as cell communication, cell-cell signaling, response to external stimulus, signal transduction and cell movement, leukocyte homeostasis, among others (Figure 5D). This result supports the close relationship of those genes with a proper decidualization through PR signalling.
Based on these robust results that correlates our DD fingerprinting with PR, we postulated that in a non-sPE pregnancy, progesterone (P) activates its receptor (PR) and modulates the action of Indian hedgehog (IHH) in the epithelium that induces the activation of PR in the stroma compartment. The signal transduction leads to proper decidualize the human endometrial stromal cells (hESC), which in turn interact with immune system and endothelial cells to prepare the tissue microenvironment to be successfully invaded by the cytotrophoblast cells
(Figure 5E). In contrast, in sPE pregnancies, IHH expression was found downregulated, which affected the PR signalling pathway in the stromal compartment compromising decidualization, and consequently, affecting processes involved in immune system deregulation and endothelial dysfunction (Figure 5F). In detail, there were evidences of an impaired response to stimulus [e.g. IL8, CCL20 , and CNR I] and an epithelial-stromal crosstalk through IHH and FGF1, which could lead to an imbalance of hESC proliferation and differentiation. We found downregulated genes involved in those process, including PRL and WNT regulators and effectors —such as FGF1, FJX1, SFRP1, NKD1— and extracellular matrix degradation [e.g. MMP1, MMP9 and MMP11\. Other genes affected in sPE are related with AKT, which play an important role in cell proliferation and cell motility. Interestingly, ART downregulates PR-B and it has been described this modulation regulates angiogenesis. According to this, we found some affected genes associated with endothelial dysfunction through AX A , KLK3, EDN2 and PTGS2 mRNA alteration. Also, endothelial inflammation and oxidative stress likely be affected, since we found downregulated genes such as ICAM1, SELE and RBP7. Finally, immune system was prominently deregulated showing a high number of DEGs. Some examples were TN1 IL6, CXCL3 and IGHG1. Taken together, our findings reveal that the decidualization defect observed in patients with previous sPE might be derived by a deregulated progesterone signalling.
Example 2.7. Assays for assessing the proper time for collecting the samples.
The ERA test evaluates endometrial receptivity detecting the optimal day for the embryo transfer that is specific for each women. The protocol indicates to take an endometrial biopsy during the window of implantation (WOI), in a natural cycle correspond to 19-21 days of the menstrual cycle and in a hormone replacement therapy (HRT) cycle after five days of progesterone administration (120 hours from the first progesterone intake; P+5).
Our work demonstrates a decidualization transcriptional defect in vivo of endometrial biopsies obtained in the late secretory phase (1 to 6 days before menstruation started) from patients that developed severe preeclampsia (sPE) in a previous pregnancy compared with controls without sPE.
Thus, our aim was to investigate if this endometrial transcriptomic defect associated with preeclampsia could be detected at time of ERA samples collection (8 to 10 days before
menstruation started). To test this objective, we performed a global transcriptomic profiling of endometrial tissue (ERA samples) from patients in whom severe sPE developed compared with a control pregnancies.
For this purpose, we classified endometrial biopsies taken for the ERA test depending of the pregnancy obstetric clinical information in control (healthy pregnancies) or preeclampsia.
This was the number of total samples:
• Control (N= 43).
• Preeclampsia (N= 46).
Based on the ERA diagnostic these samples were classified in:
• Pre-receptive day 1 (PREdl).
• Early receptive (ER).
• Receptive (R).
• Late receptive (LR).
• Post-receptive (POST).
The distribution of samples from women with a control or severe preeclampsia (sPE) pregnancy based on the ERA result is shown in Table 5.
Global RNAseq analysis was performed using the ERA sample set of control (N=43) and sPE (N=46). The raw sequencing genes among the 89 samples were 56,638 and after normalization the number of genes included in the analysis was 19,941.
A principal component analysis (PCA) was plotted using the normalized gene expression, where each dot represents one sample. The dots were coloured based on different classification.
At Figure 8, the dots were coloured based on the ERA prediction (diagnostic): PREdl, eR, R, Late-R and POST.
This analysis showed that samples are successfully grouped based on its ERA diagnostic, so the sample classification is based on their receptivity status. Figure 8 showed how the sample clustering is based on their colour:
Next, Figure 9 showed the dots colour based of the disease status: control or preeclampsia. While, we can see at the Figure 9, that the disease is not an important factor for grouping samples. They are mixed together.
In conclusion, transcriptomic profile of endometrial samples collected when ERA test is performed (19-21 day of menstrual cycle) failed to successfully cluster the control and preeclampsia samples. In conclusion, our work demonstrates that the samples should be collected during late secretory menstrual cycle (1 to 6 before menstrual cycle ends) to detect transcriptional differences between control and preeclampsia that let successful classify the samples in the two groups.
Example 2.8. Selection of differentially expressed (DE) genes
A gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.
A gene deregulation could be responsible of the physiological differences associated with severe preeclampsia.
We performed an RNA sequencing to obtain the gene expression profiling of each sample included in our study. The data analysis was focused on pairwise comparisons between the groups control vs severe preeclampsia (sPE), revealing the DE genes between the two groups. So, we found a high number of genes that were significantly deregulated in sPE compared with control specimens.
The parameters used to consider a gene as DE were:
Adjusted p-value with a cut-off of 0.05. The adjustment method was “FDR”.
• Level of deregulation (sPE vs control) is referred as log2 -fold-change (logFC). We used the following cut-offs: |1|, |2|, |2.5|, and |3|. In detail, genes with expression > 1 were considered upregulated, while genes with expression < -1 were downregulated. The same principle for the rest of cut-offs.
Table 6 shows the number of DE genes obtained for each cut-off of log2FC.
The DE genes were used to plot the dendrogram and principal component analysis (PCA). These plots allow us to observe the similarity or dissimilarity among samples based on the DE genes. The expected result was to obtain two separated groups (sPE and control). That distribution of samples would mean that the DE genes enable to classify samples in sPE or control.
In all cases, the result obtained was that DE genes selected by different fold change cut-off (1, 2, 2.5 and 3) segregate successfully the sPE and control samples. In all cases, only three controls were misclustered.
So, according to the log2FC, the genes were classified as shown in the following tables: Table 3 (859 genes) (log2FC >1), Table 8 (197 genes) (log2FC >2), Table 9 (90genes) (log2FC >2.5) and Table 10 (47 genes) (log2FC >3). Hgnc (Gene Nomenclature Committee), logFC (logarithim FC fold change), logCPM (logarithm counts per million), PValue (P-value) and FDR (False Discovery Rate).
Example 3. Second analysis of the global transcriptional signature of defective decidualization in vivo from sPE patients
3.1. Endometrial transcriptome alterations during decidualization in sPE
To identify transcriptomic alterations during decidualization in sPE, we applied global RNA sequencing (RNA-seq) to endometrial biopsies obtained in the late secretory phase from women who developed sPE in a previous pregnancy (n = 24) and controls who never had sPE (n = 16) (GSE172381). Clinical maternal and neonatal characteristics of the participants are summarized in Table 11.
After quality trimming and filtering, reads were aligned to the reference genome hgl9. The 40 samples produced 56,638 raw sequencing genes; after normalization, 18,301 genes were included in the analysis. Biological and technical variables for each donor were considered to discard confounding effects on the transcriptomic profile. Controls included women who had a preterm birth with no signs of infection (n = 8) and women who gave birth at full term with normal obstetric outcomes (n = 8). Transcriptomic profiles were compared by differential expression analysis, revealing no significant changes in the endometrial transcriptome between preterm and term controls (FDR > 0.05; Figure 10). Principal component analysis (PCA) supported that there was no underlying pattern of distribution depending on gestational age at delivery (Figure 10). Once we ruled out bias on controls, we randomly split samples into two cohorts, a training set (70%) and a test set (30%) (Figure 10A). Random sampling occurred within each class (sPE and controls), so overall class distribution of the data was preserved. The training set (n = 29) was used for the identification of molecular fingerprinting encoding DD in sPE, while the test set (n = 11) was used to confirm our findings. All samples in both cohorts were processed and sequenced in the same manner.
Transcriptional analysis in the training set was performed by comparing gene expression patterns in sPE (n = 17) and controls (n = 12). This comparison revealed 593 DEGs based on FDR < 0.05 and with at least 1.2 FC between groups (FC > 1.2). DEGs are shown in the volcano plot through yellow dots (Figure 10B). A total of 155 upregulated and 438 downregulated DEGs were identified as being associated with DD in sPE (Figure IOC; complete list in Table 12)).
Table 12: The 593 statistically differential expressed genes (FDR < 0.05) with at least 1.2-fold change (FC > 1.2) in sPE vs control cases obtained from RNA-seq analysis.
Downregulated transcripts include those involved in decidualization, such as MMP3, PRL, IL- 6, and IHH; and genes associated with signaling (e.g., NR4A3 and IL8), growth factors (e.g., FGF1 and FGF7), angiogenesis (e.g., EDN2 and TMEM215), and immune response (CCL20, CXCL3, and IGHG1). Upregulated genes are involved in amino acid metabolic/catabolic processes (ID02 and CAPN3), transport, and oxidore- ductase activity.
3.2. Comparison of DD transcriptomics in previous sPE in vivo vs. in vitro
We previously described DD in human endometrial stromal cells (hESCs) isolated from women with previous sPE compared to women with normal obstetric outcomes, but this finding was restricted to the stromal cell population using an in vitro decidualization cell culture model (Garrido-Gomez et al., 2017). Here, we compared DD overlapping between DEGs reported in vitro (n = 129) vs. in vivo (n = 593) in sPE compared to control women. Nine genes were overlapped between the two datasets (Figure 11A); one gene was upregulated (ERP27), and eight genes were downregulated (e.g., ISM1, MEST, MFAP2, and REEP2). The expression pattern of common genes is presented as a box plot using counts per million, corroborating significant differential expression between sPE and control (Figure 11B). Recently, in vivo transcriptomics of endometrium at single-cell resolution across the menstrual cycle were characterized (Wang et al., 2020). Transcriptome profiles of stromal fibroblasts from the late secretory phase allowed the identification of deregulated genes in sPE as associated to hESC. We found that 263 genes from the 593 DEGs in sPE vs. control are expressed by hESC (Figure
11C). Taken together, the in vivo assessment provides a broad spectrum of dysregulated transcripts comparing with previous in vitro findings, which includes a high concordance with in vivo hESC genes.
3.3. Identification of the fingerprint encoding human endometrial DD To formulate the transcriptomic signature that encodes DD detected in sPE in vivo, we selected genes with significant dysregulation (FDR < 0.05) and at least 1.4-fold increase (FC >1.4) between sPE and control with assigned EntrezID. A volcano plot shows 120 DEGs meeting these criteria included in the final DD signature (Figure 12A; complete list of genes is included in Table 13.
GO analysis of the gene signature associated with DD in sPE identified 151 enriched biological processes downregulated (FDR < 0.05). These pathways were associated with cell cycle, DNA damage response, cell signaling, cellular response, cell motility, extracellular matrix, immune response, and reproductive process (Figure 12B). All are hallmarks of impaired decidualization and sPE pathogenesis. We identified fingerprinting genes representative of the altered pathways in sPE, such as IL6 and TNF, regulating the response to bacterial molecules, MMP3 and MMP1 participating in the extracellular matrix organization, and TNF, IL8, and FGF1 implicated in the downregulated receptor signaling (Figure 12C). Functional analysis revealed that the 120 DEGs included in DD fingerprinting are implicated in pathways related to decidualization, corroborating the maternal contribution to sPE. Interestingly, the number of downregulated genes was higher than the number of upregulated genes in sPE compared to controls, suggesting that, in vivo, DD may be induced by the lack of expression of a subset of genes.
Based on the 120 genes included in the DD signature, PC A showed that sPE and control samples clustered separately in two groups, except for three control samples (C20, C21, and C22) (Figure 13A). High variance between groups was effectively captured in the first two principal components. Unsupervised hierarchical clustering analysis confirmed that gene fingerprinting effectively segregated the two groups: one encompassing mainly controls and the other mainly sPE samples (Figure 13B). The same three controls clustered with the sPE group, recreating the PC A results.
To validate the DD gene signature in an independent cohort of samples (sPE [n = 7] vs. control [n=4]), PCA based on these transcripts effectively segregated samples in two homogeneous groups (Figure 13C), corroborated by hierarchical clustering (Figure 13D). These genes successfully grouped 100% of controls and 85.7% of sPE cases supporting DD in sPE.
3.4. DD fingerprint in sPE is connected to ER1 and PR-B
Of the 120 genes in the DD signature, 94 endometrial enriched genes encode for specific proteins reported by the Human Protein Atlas (Uhlen et al., 2015). Interestingly, 45 of those genes (47.9%) were included in the transcriptome modulated by ESR1 (Hewitt et al., 2010), and 43 genes (45.7%) overlapped with the transcriptome and cistrome associated with PGR (Mazur et al., 2015; Figure 14A). Regarding target genes of ER1 and PR, the database of Human Transcription Factor Targets (hTFtarget) reported 17 genes responsive to ER1 and 50 target genes modulated by PR, based on epigenomic, CHIP-seq, or motif evidence (Zhang et al., 2020).
We evaluated the interaction between steroid receptor signaling and the proteins encoded by DD fingerprinting genes in sPE by building a dynamic network including ER1 and PR. String software (Jensen et al., 2009) was used to construct network connections visualized with Cytoscape software (Shannon et al., 2003). The interactome contained 117 nodes directly interconnected by 361 edges (Figure 14B). This DD fingerprint network showed a highly enriched protein-protein interaction (PPI) in sPE; indeed, the interconnection between nodes was significantly higher than the 93 edges expected (PPI enrichment p<1.0e-16). Clustering revealed three main modules based on their connectivity degree, with functionally relevant genes involved in gland morphogenesis, cell migration, extracellular matrix organization, stromal cell differentiation, cellular response to DNA damage stimulus, and regulation of cell cycle. The hub genes were determined by overlapping the top 10 genes obtained using two topological analysis methods in the cytoHubba plugin (Chin et al., 2014), MCC, and MNC. Five genes were selected, all of which were downregulated. Interestingly, both ER1 and PR were strongly embedded in the network and highly connected with DD fingerprinting, highlighting the interaction of hormonal receptors with notable decidualization mediators such as IHH and MSX2 validated by RT-qPCR (Figure 14C and D). Furthermore, the interactome demonstrated a direct interaction between ER1 and PR. These results support the transcriptomic dysfunction of the genes present in the DD signature through imbalanced hormone receptor signaling in sPE.
We then analyzed the expression of ESR1 and PGR in the endometrial tissue from a subset of women with prior sPE (N = 13) compared to controls (N = 9) by RT-qPCR. We found reduced expression of transcripts encoding the hormone receptors ESR1 (p<0.05) and PGR (p<0.001) in sPE patients (Figure 14E and F). In-depth expression analyses revealed that the isoform
PGR-B was significantly downregulated in sPE vs. controls (p<0.01), while the isoform PGR- A was unaffected (p>0.05) (Figure 14G and H). These results were confirmed at the protein level by immunohistochemical analysis of ER1 and PR-B in endometrial biopsies collected in the late secretory phase from women with previous sPE (n = 4) and controls (n = 4) (Figure 141 and J). Both receptors were highly expressed through the decidualized endometrium, especially in the secretory glands in controls. In contrast, their expression was greatly reduced or absent in sPE samples. These results suggest that the DD transcriptomic signature implicates dysregulated ER1 and PR-B signaling in the late secretory phase in sPE patients.
Example 3.5. Selection of differentially expressed (DE) genes
A gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.
A gene deregulation could be responsible of the physiological differences associated with severe preeclampsia.
We performed an RNA sequencing to obtain the gene expression profiling of each sample included in our study. The data analysis was focused on pairwise comparisons between the groups control vs severe preeclampsia (sPE), revealing the DE genes between the two groups. So, we found a high number of genes that were significantly deregulated in sPE compared with control specimens.
The parameters used to consider a gene as DE were:
• Adjusted p-value with a cut-off of 0.05. The adjustment method was “FDR”.
• Level of deregulation (sPE vs control) is referred as log2 -fold-change (logFC). We used the following cut-offs: |1|, |2|, |2.5|, and |3|. In detail, genes with expression > 1 were considered upregulated, while genes with expression < -1 were downregulated. The same principle for the rest of cut-offs.
Table 6 shows the number of DE genes obtained for each cut-off of log2FC.
The DE genes were used to plot the dendrogram and principal component analysis (PCA). These plots allow us to observe the similarity or dissimilarity among samples based on the DE genes. The expected result was to obtain two separated groups (sPE and control). That distribution of samples would mean that the DE genes enable to classify samples in sPE or control.
In all cases, the result obtained was that DE genes selected by different fold change cut-off (1, 2, 2.5 and 3) segregate successfully the sPE and control samples. In all cases, only three controls were misclustered. So, according to the log2FC, the genes were classified as shown in the following tables: Table 15 (445 genes) (log2FC>l), Table 16 (135 genes) (log2FC>2), Table 17 (71 genes) (log2FC > 2.5) and Table 18 (42 genes) (log2FC > 3). Hgnc (Gene Nomenclature Committee), log2FC > (logarithm FC fold change), logCPM (logarithm counts per million), PValue (P-value) and FDR (False Discovery Rate).
Example 3.6. Selection of differentially expressed (DE) genes identified in the second analysis but not identified in the first analysis A gene is declared differentially expressed if a difference or change observed in expression levels between two experimental conditions is statistically significant. In other words, genes that are significantly down-regulated or up-regulated in cases compared to controls.
We performed an RNA sequencing to obtain the gene expression profiling of each sample included in our study. The data analysis was focused on pairwise comparisons between the groups control vs severe preeclampsia (sPE), revealing the DE genes between the two groups. So, we found a high number of genes that were significantly deregulated in sPE compared with control specimens.
The parameters used to consider a gene as DE were: · Adjusted p-value with a cut-off of 0.05. The adjustment method was “FDR”.
• Level of deregulation (sPE vs control) is referred as log2 -fold-change (logFC). We used the following cut-offs: |1|, |2|, |2.5|, and |3|. In detail, genes with expression > 1 were considered upregulated, while genes with expression < -1 were downregulated. The same principle for the rest of cut-offs.
Table 20 shows the total number of DE genes that were identified in the second analysis but not in the first analysis (150 genes)
Table 21 shows the DE genes forming part of the defective decidualization fingerprinting that were identified in the second analysis but not in the second analysis (47 genes) Table 21
(ίoik' S mbol PViiliio I (
CDH24 64403 5,04321E-05 0,019683522 -1,4577474 -3
CDT1 81620 3,21621E-05 0,016856612 -1,8414058 -4
CENPH 64946 9,32773E-05 0,025983952 -1,3559079 -3
DEPDC1 55635 0,000360157 0,047190803 -1,9870333 -4
DERL3 91319 4,39346E-05 0,019188979 -1,7165019 -3
E2F1 1869 0,000293676 0,044157354 -1,6219685 -3
ENC1 8507 6,62848E-05 0,023383252 -1,7837816 -3
FBN2 2201 0,00023381 0,040847767 -1,8648233 -4
FOLH1 2346 0,000263435 0,042764987 -1,2528085 -2
GGH 8836 0,000249052 0,041734633 -1,327446 -3
GINS2 51659 7,45766E-05 0,024000579 -1,7662281 -3
ITIH5 80760 0,000190369 0,037549851 -1,4824169 -3
KCNN1 3780 0,000225184 0,040497789 -1,9915349 -4
LAMA1 284217 0,000108317 0,028091059 -1,9422547 -4
LPIN3 64900 0,000270354 0,043337549 1,39360305 3
MAD2L1 4085 0,000310262 0,045245377 -1,4938115 -3
MDK 4192 0,000108259 0,028091059 -1,3204592 -2
MND1 84057 1,01168E-05 0,011598949 -1,9364169 -4
NAT8L 339983 0,000390149 0,049923981 -1,4223337 -3
NREP 9315 0,000231216 0,040782863 -1,8059789 -3
ORAOV1P1 100873907 0,000346511 0,047035434 2,00020989 4
PAG1 55824 0,000123285 0,029421714 -1,6461354 -3
POC1A 25886 0,000182385 0,036765574 -1,2783295 -2
PODXL2 50512 5,40379E-05 0,0206515 -1,6230071 -3
PRKXP1 441733 7,34087E-05 0,024000579 1,81295237 4
PTTG1 9232 0,000132644 0,030660126 -1,7185713 -3
REEP2 51308 0,000390239 0,049923981 -1,6323242 -3
RIMBP2 23504 0,000288534 0,044157354 1,7214548 3
RIMS2 9699 0,000213307 0,040045836 1,9991805 4
RNASEH2A 10535 0,000127691 0,030030396 -1,1237793 -2
SKA 3 221150 0,000340667 0,047035434 -1,9264692 -4
TIPIN 54962 0,000179616 0,036609649 -1,0144111 -2
TK1 7083 3,06521E-05 0,016537734 -1,7629718 -3
UBE2T 29089 0,000121428 0,029421714 -1,5133385 -3
UHRF1 29128 4,54212E-05 0,019350768 -1,8413797 -4
XRCC2 7516 0,000348988 0,047035434 -1,6481011 -3
ZNF367 195828 0,000141751 0,03127971 -1,7971907 -3
*FC: indicates downregulated genes calculated as -POWER(2,-logFC)
Further aspects of the invention
1. In vitro method for the diagnosis and/or prognosis of preeclampsia which comprises:
a. Measuring the expression level of at least a gene selected from Table 3, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In vitro method, according to aspect 1, which comprises: a. Measuring the expression level of at least a gene selected from Table 4, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In vitro method, according to any of the previous aspects, which comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2fold change of at least 1 for the expression of at least a gene included in Table 3, a log2fold change of at least 2 for the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 for the expression of at least a gene included in Table 9 or a log2 fold change of at least 3 for the expression of at least a gene included in Table 10 In vitro method, according to any of the previous aspects, which comprises: a. Measuring the expression level of all genes comprised in Table 10, or in Table 9, or in Table 8, or in Table 3, or in Table 4, or in Table 3, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein a deviation in the level of expression of the genes measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis. In vitro method, according to any of the previous aspects, which comprises determining that the patient is suffering from preeclampsia or has a bad prognosis based on a
log2fold change of at least 1 for the expression of all genes included in Table 3, a log2fold change of at least 2 for the expression all genes included in Table 8, a log2 fold change of at least 2.5 for the expression all genes included in Table 9 or a log2 fold change of at least 3 for the expression of all genes included in Table 10.
6. In vitro method, according to any of the previous aspects, which comprises: a. Measuring the expression level of progesterone receptor in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and b. Wherein the determination of a lower the level of expression in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects, is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
7. In vitro method, according to any of the previous aspects, characterized in that it is a computer implemented method, which comprises: a. Entering into the computer the level of expression of the genes obtained from healthy control subjects; b. Entering into the computer the level of expression of the genes obtained in the step a) of the previous claims; c. Producing a score which is displayed on the device; d. Determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with a pre-established threshold level of expression determined in healthy control subjects.
8. In vitro method, according to any of the previous claims, wherein the biological sample is an endometrial tissue sample.
9. In vitro use of at least a gene selected from Table 3 for the diagnosis and/or prognosis of preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
10. In vitro use, according to aspect 9, of at least a gene selected from Table 4.
11. In vitro use, according to any of the aspects 8 to 10, of the progesterone receptor.
12. In vitro use, according to any of the aspects 8 to 11, wherein the biological sample is an endometrial tissue sample.
13. Kit for implementing any of the methods according to aspects 1 to 8 which comprises:
a. Reagents for measuring the level of expression of at least a gene selected from Table 3, or any combination thereof, b. Tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle. 14. Kit, according to aspect 13, which comprises: a. Reagents for measuring the level of expression of at least a gene selected from Table 4, or any combination thereof, b. Tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle. 15. Use of the kit according to aspects 13 or 14, for the diagnosis and/or prognosis of preeclampsia.
Claims
1. In vitro method for determining the risk of suffering from preeclampsia in a subject, which comprises:
(i) Measuring the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, Table 3 or Table 12 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and
(ii) wherein a deviation in the level of expression of the gene measured in step a) as compared with a reference expression level, is an indication that the patient is at risk of suffering from preeclampsia.
2. The in vitro method, according to claim 1, characterized in that the expression level of at least one gene shown in Table 21 is determined.
3. The in vitro method, according to claim 1, characterized in that the expression level of at least one gene shown in Table 4 is determined.
4. The in vitro method, according to any of the previous claims, characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 3 a log2fold change of at least 2 in the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 9 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 10.
5. The in vitro method, according to claim 4, characterized in that the expression levels of all genes comprised in Table 3, in Table 8, in Table 9 or in Table 10 are measured.
6. The in vitro method, according to claim 1, characterized in that the expression level of at least one gene shown in Table 13 is determined.
7. The in vitro method, according to claim 6, characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 in the expression of at least a gene included in Table 15 a log2fold change of at least 2 in the expression of at least a gene included in Table 16, a log2 fold change of at least 2.5 in the expression of at least a gene included in Table 17 or a log2 fold change in at least 3 for the expression of at least a gene included in Table 18.
8. The in vitro method, according to claim 7, characterized in that the expression levels of all genes comprised in Table 15, in Table 16, in Table 17 or in Table 18 are measured.
9. In vitro method for the prognosis of preeclampsia which comprises:
(i) Measuring the expression level of at least a gene selected from Table 3, or any combination thereof, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and
(ii) Wherein a deviation in the level of expression of the gene measured in step a) as compared with a pre-established threshold level of expression determined in healthy control subjects is an indication that the patient is suffering from preeclampsia or has a bad prognosis.
10. In vitro method, according to claim 1, characterized in that step (i) comprises the measurement of the expression level of at least a gene selected from Table 4, or any combination thereof.
11. The in vitro method, according to any of claims 8 or 9 characterized in that the patient is determined of being at risk of suffering from preeclampsia if the deviation in the level of expression of the at least one gene is a log2fold change of at least 1 for the expression of at least a gene included in Table 3, a log2fold change of at least 2 for the expression of at least a gene included in Table 8, a log2 fold change of at least 2.5 for the expression of at least a gene included in Table 9 or a log2 fold change of at least 3 for the expression of at least a gene included in Table 10.
12. The in vitro method, according to any of the claims 8 to 10 which comprises measuring the expression level of all genes comprised in Table 10, or in Table 9, or in Table 8, or in Table 4, or in Table 3.
13. An in vitro method for determining the risk of suffering from preeclampsia, which comprises:
(i) Measuring the expression level of the progesterone receptor or of the estrogen receptor 1 in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle, and
(ii) Wherein reduced expression levels of expression of the progesterone receptor or of the estrogen receptor 1 in step (i) as compared with a pre-established threshold level of expression, is an indication that the patient is at risk of suffering from preeclampsia.
14. In vitro method, according to any of the previous claims, characterized in that it is a computer implemented method, which comprises:
(i) Entering into the computer the reference expression levels of the genes which are measured in step (i);
(ii) Entering into the computer the level of expression of the genes obtained in the step (i) of the previous claims;
(iii) Producing a score which is displayed on the device;
(iv) Determining that the patient is suffering from preeclampsia or has a bad prognosis based on a log2 fold change of at least 1, at least 2, at least 2.5 or at least 3, when compared with the reference expression levels.
15. In vitro method, according to any of the previous claims, wherein the biological sample is an endometrial tissue sample.
16. A method for predicting the risk of suffering preeclampsia and treating preeclampsia in a subject which comprises:
(i) Determining the risk of suffering from preeclampsia in said subject by a method according to any of claims 1 to 15,
(ii) Monitoring the subjects which have been determined in step (i) to be at high
risk of suffering preeclampsia in order to detect the appearance of preeclampsia and
(iii) Administering to said subject a therapeutically effective dose or amount of a therapy aimed at treating preeclampsia once preeclampsia has been detected.
17. In vitro use of the expression level of at least one gene, wherein the at least one gene is selected from the group consisting essentially of the genes shown in Table 20, Table 3 or Table 12 for determining the risk of suffering from preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
18. The in vitro use according to claim 17 wherein the at least one gene is selected from the genes shown in Table 21, in Table 4 or in Table 13.
19. The in vitro use according to claim 18 wherein the at least one gene includes all the genes shown in Table 3, Table 8, Table 9 or Table 10.
20. The in vitro use according to claim 17 wherein the at least one gene includes all the genes shown in Table 15, Table 16, Table 17 or Table 18.
21. In vitro use of the expression level of the progesterone receptor gene or of the estrogen receptor 1 gene for determining the risk of suffering from preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
22. In vitro use of at least a gene selected from Table 3 for the prognosis of preeclampsia, in a biological sample which has been obtained from the patient 1 to 6 days before the end of the menstrual cycle.
23. The in vitro use according to claim 19 wherein the gene is selected from Table 4.
24. The in vitro use, according to any one of claims 15 to 20 wherein the biological sample is an endometrial tissue sample.
25. Kit for implementing any of the methods according to any one of claims 1 to 21, which comprises:
(i) reagents for measuring the level of expression of at least one gene selected from the genes shown in Table 20, in Table 3, in Table 12 or any combination thereof and
(ii) tools for obtaining a biological sample 1 to 6 days before the end of the menstrual cycle.
23. Kit, according to claim 22 wherein component (i) contains reagents for measuring the level of expression of at least one gene selected from the genes shown in Table 21, in Table 4, or in Table 13
26. Use of the kit according to claims 22 or 23, for determining the risk of suffering from preeclampsia.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP21382116 | 2021-02-12 | ||
EP21382116.8 | 2021-02-12 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2022171318A1 true WO2022171318A1 (en) | 2022-08-18 |
Family
ID=74732846
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/EP2021/079425 WO2022171318A1 (en) | 2021-02-12 | 2021-10-22 | In vitro method for determining the risk of suffering from preeclampsia |
Country Status (1)
Country | Link |
---|---|
WO (1) | WO2022171318A1 (en) |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050255114A1 (en) * | 2003-04-07 | 2005-11-17 | Nuvelo, Inc. | Methods and diagnosis for the treatment of preeclampsia |
WO2010033553A2 (en) * | 2008-09-16 | 2010-03-25 | University Of Florida Research Foundation Inc. | Gene expression related to preeclampsia |
US20130203606A1 (en) | 2010-02-25 | 2013-08-08 | Advanced Liquid Logic Inc | Method of Preparing a Nucleic Acid Library |
US20200271660A1 (en) * | 2017-09-05 | 2020-08-27 | Igenomix S.L. | Methods and devices for detecting biomarkers associated with preeclampsia |
-
2021
- 2021-10-22 WO PCT/EP2021/079425 patent/WO2022171318A1/en active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050255114A1 (en) * | 2003-04-07 | 2005-11-17 | Nuvelo, Inc. | Methods and diagnosis for the treatment of preeclampsia |
WO2010033553A2 (en) * | 2008-09-16 | 2010-03-25 | University Of Florida Research Foundation Inc. | Gene expression related to preeclampsia |
US20130203606A1 (en) | 2010-02-25 | 2013-08-08 | Advanced Liquid Logic Inc | Method of Preparing a Nucleic Acid Library |
US20200271660A1 (en) * | 2017-09-05 | 2020-08-27 | Igenomix S.L. | Methods and devices for detecting biomarkers associated with preeclampsia |
Non-Patent Citations (15)
Title |
---|
"Gestational Hypertension and Preeclampsia", OBSTET GYNECOL, vol. 133, no. 1, January 2019 (2019-01-01), pages 1 |
"Molecular Cloning: A Laboratory Manual", 1989, COLD SPRING HARBOR LABORATORY PRESS |
"UniProtKB", Database accession no. P06401-1 |
BEAUCAGECARUTHERS, TETRAHEDRON LETTS, vol. 22, 1981, pages 1859 - 1862 |
CONRAD KIRK P ET AL: "Emerging role for dysregulated decidualization in the genesis of preeclampsia", PLACENTA, W.B. SAUNDERS, GB, vol. 60, 9 June 2017 (2017-06-09), pages 119 - 129, XP085298279, ISSN: 0143-4004, DOI: 10.1016/J.PLACENTA.2017.06.005 * |
DE ANDRES ET AL., BIOTECHNIQUES, vol. 18, 1995, pages 42044 |
ERRATUM, INT J GYNAECOL OBSTET, vol. 146, no. 3, September 2019 (2019-09-01), pages 390 - 391 |
GARRIDO-GOMEZ T ET AL: "S-159- Endometrial Transcriptomic Fingerprinting Associated to In Vivo Decidualization Resistance in Severe Preeclampsia", vol. 27, no. S1, 2 March 2020 (2020-03-02), XP055825681, Retrieved from the Internet <URL:https://link.springer.com/content/pdf/10.1007/s43032-020-00176-9.pdf> * |
GARRIDO-GÓMEZ TAMARA ET AL: "DEFECTIVE DECIDUALIZATION AFTER SEVERE PREECLAMPSIA IS CONNECTED TO DYSREGULATION OF PROGESTERONE RECEPTOR B AND ESTROGEN RECEPTOR 1", FERTILITY AND STERILITY, ELSEVIER, AMSTERDAM, NL, vol. 116, no. 3, 1 September 2021 (2021-09-01), XP086783195, ISSN: 0015-0282, [retrieved on 20210917], DOI: 10.1016/J.FERTNSTERT.2021.07.116 * |
GARRIDO-GOMEZ TAMARA ET AL: "Defective decidualization during and after severe preeclampsia reveals a possible maternal contribution to the etiology", vol. 114, no. 40, 3 October 2017 (2017-10-03), US, pages E8468 - E8477, XP055825620, ISSN: 0027-8424, Retrieved from the Internet <URL:https://www.pnas.org/content/pnas/114/40/E8468.full.pdf> DOI: 10.1073/pnas.1706546114 * |
GARRIDO-GOMEZ TAMARA ET AL: "Disrupted PGR-B and ESR1 signaling underlies preconceptional defective decidualization linked to severe preeclampsia", MEDRXIV, 24 July 2021 (2021-07-24), XP055881948, Retrieved from the Internet <URL:https://www.medrxiv.org/content/10.1101/2021.07.22.21260977v1.full.pdf> [retrieved on 20220120], DOI: 10.1101/2021.07.22.21260977 * |
NEEDHAM-VAN DEVANTER ET AL., NUCLEIC ACIDS RES., vol. 12, 1984, pages 6159 - 6168 |
POON LC ET AL.: "The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention", INT J GYNAECOL OBSTET, vol. 145, May 2019 (2019-05-01), pages 1 - 33 |
RUPPLOCKER, LAB INVEST, vol. 56, 1987, pages A67 |
SAMBROOK, J. ET AL.: "Molecular cloning: A Laboratory Manual", vol. 1-3, 2001, COLD SPRING HARBOR LABORATORY PRESS |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gong et al. | The RNA landscape of the human placenta in health and disease | |
Nishizawa et al. | Microarray analysis of differentially expressed fetal genes in placental tissue derived from early and late onset severe pre-eclampsia | |
EP3037554B1 (en) | Methods for selecting competent oocytes and competent embryos with high potential for pregnancy outcome | |
US10851415B2 (en) | Molecular predictors of sepsis | |
KR20150070308A (en) | Systems and methods for determining the probability of a pregnancy at a selected point in time | |
CA2778926A1 (en) | Means and methods for non-invasive diagnosis of chromosomal aneuploidy | |
JP2015527870A (en) | Method and device for assessing the risk of presumed births developing a condition | |
Garrido-Gomez et al. | Disrupted PGR-B and ESR1 signaling underlies defective decidualization linked to severe preeclampsia | |
Bahado‐Singh et al. | Placental DNA methylation changes in detection of tetralogy of Fallot | |
US9090938B2 (en) | Methods for selecting competent oocytes and competent embryos with high potential for pregnancy outcome | |
CN108796065B (en) | Application of FAM127A in pregnancy diseases | |
CA2780430A1 (en) | Genes differentially expressed by cumulus cells and assays using same to identify pregnancy competent oocytes | |
CN108866181B (en) | Application of MBOAT1 gene in preeclampsia period | |
CN113454241A (en) | Nucleic acid biomarkers of placental dysfunction | |
WO2022171318A1 (en) | In vitro method for determining the risk of suffering from preeclampsia | |
Luo et al. | Transcriptome‐wide high‐throughput m6A sequencing of differential m6A methylation patterns in the decidual tissues from RSA patients | |
US20140171371A1 (en) | Compositions And Methods For The Diagnosis of Schizophrenia | |
US11685950B2 (en) | Method of diagnosing and treating acute rejection in kidney transplant patients | |
KR102505617B1 (en) | Urinary exosome-derived miRNA gene biomarkers for diagnosis of T cell-mediated rejection in kidney allografts and use thereof | |
CN108728531B (en) | Application of biomarker CBX8 in preeclampsia diagnosis and treatment | |
WO2019168971A1 (en) | Methods for assessing risk of increased time-to-first-conception | |
WO2009027524A1 (en) | Diagnostic of immune graft tolerance using tmtc3 gene expression levels | |
KR102545543B1 (en) | Urinary exosome-derived miRNA gene biomarkers for diagnosis of BK virus nephropathy in kidney allografts and use thereof | |
EP4183888A1 (en) | Mirna signature for identification of the receptive endometrium | |
Murikipudi et al. | Circulating miRNA As Prognostic Biomarker During Estrus of Buffalo |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21799017 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 21799017 Country of ref document: EP Kind code of ref document: A1 |