WO2020123404A2 - Nucleic acid biomarkers for placental dysfunction - Google Patents
Nucleic acid biomarkers for placental dysfunction Download PDFInfo
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- WO2020123404A2 WO2020123404A2 PCT/US2019/065277 US2019065277W WO2020123404A2 WO 2020123404 A2 WO2020123404 A2 WO 2020123404A2 US 2019065277 W US2019065277 W US 2019065277W WO 2020123404 A2 WO2020123404 A2 WO 2020123404A2
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- nucleic acid
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- 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
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- 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
- C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA
Definitions
- Placental dysfunction most commonly manifested as preeclampsia or intrauterine growth restriction, is an important cause of maternal and fetal morbidity and mortality in both the developing and developed world placental insufficiency.
- placental dysfunction linked to preterm birth (PTB), preeclampsia, intrauterine growth restriction, preterm labor, preterm premature rupture of membranes, late spontaneous abortion and abruption placentae It is thought that placental dysfunction arises from abnormal trophoblast differentiation and/or invasion, events that occur in the first trimester of pregnancy, but become clinically apparent only in the late second and third trimesters.
- Optimal surveillance and management of placental dysfunction, as well as the development of effective therapies, have been hampered by the lack of methods for early and accurate identification of pregnancies at risk for this disorder.
- MicroRNAs are non-coding, 21-25 nucleotide, regulatory RNAs that affect the stability and/or translational efficiency of messenger-RNA (mRNAs).
- mRNAs messenger-RNA
- the present invention addresses this need and provides related advantages.
- the present invention provides compositions and methods for determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy.
- the invention provides a panel of isolated nucleic acid biomarkers comprising two or more of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a panel of isolated nucleic acid biomarkers comprising two or more of the nucleic acid biomarkers set forth in Tables 3-6, 15, 17 or 18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-6, 15, 17 or 18.
- the invention provides a biomarker panel comprising two or more of the isolated nucleic acid biomarkers selected from the group consisting of hsa-miR- 423-3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-4732-5p, hsa-miR- 1273h-3p and hsa-miR-941.
- the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa-miR-331-3p and/or hsa-miR-941. In some embodiments, the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa-miR-423-3p, hsa- miR-516b-5p, hsa-miR-4732-5p, and/or hsa-miR-1273h-3p. In some embodiments, the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa-miR-4732-5p, hsa- miR-516b-5p, and/or hsa-miR-941.
- the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-1273h- 3p, and/or hsa-miR-516b-5p.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers selected from the group consisting of hsa-miR-423- 3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-4732-5p, hsa-miR-1273h-3p and hsa-miR-941.
- the nucleic acid biomarkers selected from the group consisting of hsa-miR-423- 3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-4732-5p, hsa-miR-1273h-3p and hs
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-33 l-3p and/or hsa-miR-941.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-4732-5p, and/or hsa-miR-1273h-3p.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-4732-5p, hsa-miR-516b-5p, and/or hsa-miR-941.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-1273h-3p, and/or hsa-miR-516b-5p.
- the invention provides a pair of biomarker selected from the group consisting of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a panel of isolated nucleic acid biomarkers comprising a pair of biomarkers selected from the group consisting of the biomarker pairs set forth in Tables 7-10 or 16-18.
- the invention provides a pair of nucleic acid biomarkers selected from the group consisting of the biomarker pairs set forth in Tables 7-10 or 16-18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of the biomarker pairs set forth in Tables 7-10 or 16-18.
- the invention provides a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa- miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR- 1273h-3p/hsa-miR-3173-5p, hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa4et-7i-5p/hsa-miR-155-5p, hsa-let-7b-5p/hsa-miR-155-5p, hsa pair of nucleic acid
- the invention provides a panel of isolated nucleic acid biomarkers comprising a pair of biomarkers selected from the group consisting of hsa-miR- 127-3 p/hsa-miR- 485-5p, hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155- 5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-451a/hsa-miR-155-5p, hsa- miR- 125a-5p/hsa-miR-155-5p, hsa-let-7i-5p/hsa-miR-155-5p, hsa-let-7b-5p/hs
- the pair of biomarkers is selected from the group consisting of hsa-miR- 127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR- 485-5p, hsa-miR- 182-5p/hsa-miR-485-5p, and hsa-miR-150-3p/hsa-miR-193b-5p.
- the pair of biomarkers is selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR- 155-5p/hsa-miR- 3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-378e/hsa- miR-221-5p, and hsa-miR-345-5p/hsa-miR-324-3p.
- the pair of biomarkers is selected from the group consisting of hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-150-3p/hsa-miR-193b-5p, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-1285-3p/hsa- mir-378c.
- the pair of biomarkers is selected from the group consisting of hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa-let-7i-5p/hsa-miR-155- 5p, hsa4et-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa-miR-155-5p, hsa-miR- 127-3 p/hsa-miR- 485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR-18
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, wherein the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, correspond to a pair of biomarkers selected from the group consisting of hsa-miR- 127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR- 155-5p/hsa-miR-3173-5p, hsa-miR- 1273h-3p/hsa-miR-3173-5p, hsa-miR-451a/hsa- miR-155-5p, hsa-mi
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, wherein the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, correspond to a pair of biomarkers selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR- 485-5p, hsa-miR-182-5p/hsa-miR-485-5p, and hsa-miR-150-3p/hsa-miR-193b-5p.
- a pair of biomarkers selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, wherein the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, correspond to a pair of biomarkers selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR- 3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-378e/hsa- miR-221-5p, and hsa-miR-345-5p
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, wherein the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, correspond to a pair of biomarkers selected from the group consisting of hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-150-3p/hsa-miR-193b-5p, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-1285-3p/hsa- mir-378c.
- a pair of biomarkers selected from the group consisting of hsa-m
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, wherein the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, correspond to a pair of biomarkers selected from the group consisting of hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa-let-7i-5p/hsa-miR-155- 5p, hsa-let-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa-miR-155-5p, hsa-miR- 127-3 p/hsa-miR- 485-5p, hsa-miR-26b
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of two or more of the nucleic acid biomarkers listed in Tables 3-11 or 15-18 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- labeled and/or amplified nucleic acid molecules for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18 in a biological sample obtained from
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of two or more of the nucleic acid biomarkers listed in Tables 3-6, 15, 17 or 18 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-6, 15, 17 or 18 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- labeled and/or amplified nucleic acid molecules for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-6, 15, 17 or 18 in a
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of two or more of the nucleic acid biomarkers selected from the group consisting of hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-4732-5p, hsa-miR-1273h-3p and hsa-miR-941 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers selected from the group consisting of hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR- 4732-5p, hsa-miR-1273h-3p and hsa-miR-941 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-331-3p and/or hsa-miR-941 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-331-3p and/or hsa-miR-941 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-4732-5p, and/or hsa-miR-1273h-3p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-4732-5p, and/or hsa- miR-1273h-3p in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- labeled and/or amplified nucleic acid molecules for example, amplified labeled nucleic acid
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-4732-5p, hsa-miR-516b-5p, and/or hsa-miR-941 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-4732-5p, hsa-miR-516b-5p, and/or hsa-miR-941 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-1273h-3p, and/or hsa-miR-516b-5p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-1273h-3p, and/or hsa- miR-516b-5p in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding to the pregnant female
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-451a/hsa- miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa4et-7i-5p/hsa-miR-
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa-miR-381-3p, hsa- miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR- 3173-5p, hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hs
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR- 182-5p/hsa-miR-485-5p, and hsa-miR- 150-3 p/hsa-miR-193b-5p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the method further comprises the step of providing a score corresponding
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa- miR-98-5p/hsa-miR-485-5p, hsa-miR-182-5p/hsa-miR-485-5p, and hsa-miR-150-3p/hsa-miR- 193b-5p in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-378e/hsa-miR-221- 5p, and hsa-miR-345-5p/hsa-miR-324-3p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa- miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7-5p/hsa-miR- 941, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-345-5p/hsa-m
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR- 150-3p/hsa-miR-193b-5p, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-1285-3p/hsa-mir-378c in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR- 7-5p/hsa-miR-941, hsa-miR-150-3p/hsa-miR-193b-5p, hsa-miR-378e/hsa-miR-221-5p, and hsa- miR-1285-3p/hsa-mir-378c in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa-let-7i-5p/hsa-miR-155-5p, hsa- let-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa-miR-155-5p, hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa-let- 7i-5p/hsa-miR-155-5p, hsa-let-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa-miR-155-5p, hsa- miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR
- the method further comprises the step of providing a score corresponding to the pregnant female’s risk of developing placental dysfunction.
- the risk score is calculated based on a ratio of data values.
- data transformation is applied before or after the ratio is determined.
- Figures 1 A and IB show Principle Component Analysis plots. Principle Component Analysis for the extracellular miRNA data (all possible reversals) using the log values of the ratios ( Figure 1 A) or the ratios of log values ( Figure IB) as the features.
- the present disclosure is based, generally, on the discovery that the concentration of certain extracellular microRNA (miRNA) biomarkers present in the maternal circulation during pregnancy predicts subsequent risk of developing placental dysfunction later in the pregnancy.
- miRNA extracellular microRNA
- the concentration of miRNA in the maternal circulation is altered in women who subsequently develop placental dysfunction.
- expression levels of these miRNA biomarkers can be measured from blood samples, thereby providing a minimally-invasive means for prediction of placental dysfunction, which can manifest as preeclampsia, intrauterine growth restriction, preterm birth (PTB), preterm labor, preterm premature rupture of membranes, late spontaneous abortion and abruption placentae.
- the present disclosure is further specifically based, in part, on the unexpected discovery that single-miRNA biomarker and pairs of miRNA biomarkers disclosed herein can be utilized in methods of predicting a pregnant female’s risk of developing placental dysfunction later in the pregnancy.
- Each of the miRNA biomarkers and clinical variables disclosed herein, either alone or as components of pairs, ratios and/or reversal pairs serve as biomarkers for determining a pregnant women’s risk of developing placental dysfunction later in the pregnancy.
- a reversal value is the ratio of the abundance of an up regulated biomarker over a down regulated biomarker and serves to both normalize variability and amplify diagnostic signal.
- the invention lies, in part, in the selection of particular biomarkers that, when paired together, can accurately determine a pregnant female’s risk of developing placental dysfunction later in the pregnancy. Accordingly, it is human ingenuity in selecting the specific biomarkers that are informative upon being paired, for example, in novel reversals, and/or the data transformations, for example the ratio of log values, in forming said reversals, that underlies the present invention.
- the disclosure provides single-miRNA biomarkers and pairs of miRNA biomarkers as well as associated panels, methods and kits for determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy.
- the invention also contemplates use of biomarker variants that are at least 90% or at least 95% or at least 97% identical to the exemplified sequences and that are now known or later discovered and that have utility for the methods of the invention. These variants may represent polymorphisms, splice variants, mutations, and the like.
- the instant specification discloses multiple art-known proteins in the context of the invention and provides exemplary peptide sequences that can be used to identify these proteins.
- additional sequences or other information can easily be identified that can provide additional characteristics of the disclosed biomarkers and that the exemplified references are in no way limiting with regard to the disclosed nucleic acid.
- Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine.
- Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine.
- the biological sample is selected from the group consisting of whole blood, plasma, and serum.
- the biological sample is serum.
- nucleic acid can be detected through a variety of assays and techniques known in the art.
- the miRNA biomarkers that can be components of reversal pairs described herein include, for example, the miRNA biomarkers set forth in Tables 7-10 or 16-18.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy, the method comprising measuring in a biological sample obtained from the pregnant female a reversal value for one or more of the biomarker pairs set forth in Tables 7-10 or 16-18.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy, the method comprising measuring in a biological sample obtained from the pregnant female a reversal value for one pair of biomarkers selected from the biomarker pairs set forth in Tables 7-10 or 16-18.
- the invention provides a pair of isolated biomarkers selected from the biomarker pairs set forth in Tables 7-10 or 16-18, wherein the pair of biomarkers exhibits a higher ratio in pregnant females that will develop placental dysfunction later in the pregnancy relative to pregnant females that will not develop placental dysfunction.
- the present invention provides a composition comprising a pair of isolated biomarkers selected from the group consisting of the biomarker pairs listed in Tables 7-10 or 16-18, wherein the pair of biomarkers exhibits a higher ratio in pregnant females that will develop placental dysfunction later in the pregnancy relative to pregnant females that will not develop placental dysfunction.
- the sample is obtained between 18 and 21 weeks of GABD. In further embodiments, the sample is obtained between 23 and 28 weeks of GABD. In some embodiments, the sample is obtained between 18 and 28 weeks of GABD. In some embodiments,
- the sample is obtained between 18 and 36 weeks of GABD. In further embodiments, the sample is obtained between 18 and 36 weeks of GABD.
- the sample is obtained between 19 and 21 weeks of GABD. In some embodiments, the sample is obtained between 20 and 22 weeks of GABD. In some embodiments, the sample is obtained between 21 and 23 weeks of GABD. In further embodiments, the sample is obtained between 22 and 24 weeks of GABD. In additional embodiments, the sample is obtained between 23 and 25 weeks of GABD. In some embodiments, the sample is obtained between 24 and 26 weeks of GABD. In further embodiments, the sample is obtained between 25 and 27 weeks of GABD. In additional embodiments, the sample is obtained between 26 and 28 weeks of GABD. In some embodiments, the sample is obtained between 27 and 29 weeks of GABD. In further embodiments, the sample is obtained between 28 and 30 weeks of GABD. In additional embodiments, the sample is obtained between 29 and 31 weeks of GABD. In some embodiments, the sample is obtained between 19 and 21 weeks of GABD. In some embodiments, the sample is obtained between 20 and 22 weeks of GABD. In some embodiments, the sample is obtained between 21 and 23 weeks of GABD. In further embodiments, the sample
- the sample is obtained between 30 and 32 weeks of GABD. In further embodiments, the sample is obtained between 31 and 33 weeks of GABD. In additional embodiments, the sample is obtained between 32 and 34 weeks of GABD. In some
- the sample is obtained between 33 and 35 weeks of GABD. In further embodiments, the sample is obtained between 34 and 36 weeks of GABD. In additional embodiments, the sample is obtained between 18 and 21 weeks of GABD.
- the sample is obtained between 119 and 202 days of GABD. In further embodiments, the sample is obtained between 119 and 152 days of GABD. In some embodiments, the sample is obtained between 138 and 172 days of GABD. In further embodiments, the sample is obtained between 156 and 196 days of GABD.
- biomarker variants that are about 90%, about 95%, or about 97% identical to the exemplified sequences.
- Variants include polymorphisms, splice variants, mutations, and the like.
- compositions and methods of the invention also can include clinical variables, including but not limited to, maternal characteristics, medical history, past pregnancy history, and obstetrical history.
- additional clinical variables can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, parity, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight, low or high body mass index, diabetes, diabetes mellitus, chronic diabetes, chronic diabetes mellitus, chronic hypertension, urogenital infections (i.e. urinary tract infection), asthma, anxiety and depression, asthma, hypertension, hypothyroidism, high body mass index (BMI), low BMI, BMI.
- BMI body mass index
- Demographic risk indicia for preterm birth can include, for example, maternal age,
- Additional clinical variables useful for as markers can be identified using learning algorithms known in the art, such as linear discriminant analysis, support vector machine classification, recursive feature elimination, prediction analysis of microarray, logistic regression, CART, FlexTree, LART, random forest, MART, and/or survival analysis regression, which are known to those of skill in the art and are further described herein.
- learning algorithms known in the art, such as linear discriminant analysis, support vector machine classification, recursive feature elimination, prediction analysis of microarray, logistic regression, CART, FlexTree, LART, random forest, MART, and/or survival analysis regression, which are known to those of skill in the art and are further described herein.
- the present disclosure describes and exemplifies various models and corresponding biomarkers that perform at high levels of accuracy and precision in determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy
- Models known in the art include, without limitation, linear discriminant analysis, support vector machine classification, recursive feature elimination, prediction analysis of microarray, logistic regression, CART, FlexTree, LART, random forest, MART, and/or survival analysis regression.
- performance of a model can be evaluated based on accuracy.
- accuracy can be expressed as the percentage of time, for example, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 70%, 71%, 72%,
- the present disclosure is based in part on the surprising discovery that the selection of certain biomarkers and/or clinical variables enables determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy.
- the terms“comprises,”“comprising,”“includes,”“including,” “contains,”“containing,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, product-by-process, or composition of matter.
- the term“panel” refers to a composition, such as an array or a collection, comprising one or more biomarkers.
- the term can also refer to a profile or index of expression patterns of one or more biomarkers described herein.
- the number of biomarkers useful for a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values.
- the terms“isolated” and“purified” generally describes a composition of matter that has been removed from its native environment (e.g ., the natural environment if it is naturally occurring), and thus is altered by the hand of man from its natural state so as to possess markedly different characteristics with regard to at least one of structure, function and properties.
- An isolated protein or nucleic acid is distinct from the way it exists in nature and includes synthetic peptides and proteins.
- biomarker refers to a biological molecule, a fragment of a biological molecule, or a clinical variable the change and/or the detection of which can be correlated with a particular physical condition or state.
- the terms“marker” and“biomarker” are used
- biomarkers of the present invention are associated with a discrimination power between pregnant females that will develop placental dysfunction later in the pregnancy versus those that will not develop placental dysfunction.
- biomarkers include any suitable analyte, but are not limited to, biological molecules comprising nucleotides, nucleic acids, nucleosides, amino acids, sugars, fatty acids, steroids, metabolites, peptides, polypeptides, proteins, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof (e.g., glycoproteins, ribonucleoproteins, lipoproteins).
- the term also encompasses miRNAs and portions or fragments of a miRNAs.
- the term“reversal” refers to the ratio of the measured abundance of an upregulated analyte over that of a down-regulated analyte.
- transformation of the data can be applied prior to or after taking the ratio, as disclosed herein.
- the term“reversal pair” refers to biomarkers in pairs that exhibit a change in value between the classes being compared.
- the detection of reversals in analyte (i.e. miRNA) concentrations eliminates the need for data normalization or the establishment of population-wide thresholds.
- the corresponding reversal pair wherein individual biomarkers are switched between the numerator and denominator.
- the term“reversal value” refers to the ratio of the abundance of two analytes and serves to both normalize variability and amplify diagnostic signal.
- a reversal value refers to the ratio of the abundance of an up-regulated (interchangeably referred to as “over-abundant,” up-regulation as used herein simply refers to an observation of abundance) analyte over a down-regulated analyte (interchangeably referred to as“under-abundant,” down- regulation as used herein simply refers to an observation of relative abundance).
- a reversal value refers to the ratio of an up-regulated analyte over an up-regulated analyte, where one analyte differs in the degree of up-regulation relative the other analyte.
- a reversal value refers to the ratio a down-regulated analyte over a down- regulated analyte, where one analyte differs in the degree of down-regulation relative the other analyte.
- a reversal value refers to the ratio of a regulated analyte (up or down) and an analyte that is un-regulated. In this case the un-regulated analyte can still serve to normalize.
- a reversal value refers to the ratio of two analytes that are un regulated or whose directions of regulation are unknown. In this case, the un-regulated analytes can still serve to normalize each other and to reveal a diagnostic signal.
- One advantageous aspect of a reversal is the presence of complementary information in the two analytes, so that the combination of the two is more diagnostic of the condition of interest than either one alone.
- the combination of the two analytes increases signal-to- noise ratio by compensating for biomedical conditions not of interest, pre-analytic variability and/or analytic variability.
- a subset can be selected based on individual univariate performance. Additionally, a subset can be selected based on bivariate or multivariate performance in a training set, with testing on held-out data or on bootstrap iterations.
- logistic or linear regression models can be trained, optionally with parameter shrinkage by LI or L2 or other penalties, and tested in leave-one-out, leave-pair-out or leave-fold-out cross-validation, or in bootstrap sampling with replacement, or in a held-out data set.
- the ratio of the abundance of two analytes can be used to identify robust and accurate classifiers and predict a pregnant female’s risk of developing placental dysfunction later in the pregnancy
- a ratio of biomarkers in the methods disclosed herein corrects for variability that is the result of human manipulation after the removal of the biological sample from the pregnant female. Such variability can be introduced, for example, during sample collection, processing, depletion, digestion or any other step of the methods used to measure the biomarkers present in a sample and is independent of how the biomarkers behave in nature.
- the invention generally encompasses the use of a reversal pair in a method of diagnosis or prognosis to reduce variability and/or amplify, normalize or clarify diagnostic signal.
- reversal value can refer to the ratio of the abundance of an up regulated analyte over a down regulated analyte and serves to both normalize variability and amplify diagnostic signal
- a pair of biomarkers of the invention could be treated in a classifier by any other means, for example, by subtraction, addition or
- a value can be mathematically converted to a different value and used to determine a ratio.
- reversals can be constructed as the ratios of the logarithm (log) values.
- ratios can be mathematically converted, for example, as the log of the ratioed values (see Example 2 and Figure 1).
- the methods disclosed herein encompass the measurement of biomarker pairs by such other means. A person skilled in the art will readily understand suitable data
- transformations that can be applied to identify biomarkers predictive of placental dysfunction, including the data transformations disclosed herein.
- Exemplary transformations include, but are not limited to, box-cox, root, inverse, rank and log.
- Such data transformations are well known in the art, for example, root (where the root transformation is selected as appropriate for the data set, such as 2, 3, 4, and higher, as appropriate), inverse (1/X), rank (assigning to an ordered list based on appropriate criteria), and so forth, as is well known in the art.
- This method is advantageous because it provides the simplest possible classifier that may be independent of data normalization, helps to avoid overfitting, and results in a very simple experimental test that is easy to implement in the clinic.
- “reversal” it refers to the identification of analyte pairs where the relative expression (rank order) of each member of a pair reverses in the two conditions studied (e.g. cancer vs not cancer, placental dysfunction vs not). Reversal, as it is used here, allows for there to be opposing regulation of the two members of the pair (e.g., up or down), but does not require that their rank order in abundance to“reverse” in the different clinical conditions.
- marker pairs based on changes in reversal values that are independent of data normalization enabled the development of the clinically relevant biomarkers disclosed herein. Because quantification of any single protein is subject to uncertainties caused by measurement variability, normal fluctuations, and individual related variation in baseline expression, identification of pairs of markers that may be under coordinated, systematic regulation enables robust methods for diagnosis and prognosis.
- univariate and bivariate biomarkers disclosed herein can be used as biomarkers, either singly, in combinations of 2 or more biomarkers, as panels, or in combination with other variables (for example, proteins, metabolites, other molecules, clinical factors, and/or demographic factors) to predict placental dysfunction, such as preeclampsia, as disclosed herein.
- other variables for example, proteins, metabolites, other molecules, clinical factors, and/or demographic factors
- the biological sample is selected from the group consisting of whole blood, plasma, and serum. In one embodiment, the biological sample is serum. In one embodiment, the sample is obtained between 18 and 21 weeks of gestational age. In an additional embodiment, the sample is obtained between 23 and 28 weeks of gestational age. In a further embodiment, the sample is obtained between 18 and 28 weeks of gestational age. In some embodiments, the sample is obtained between 119 and 202 days of gestational age. In further embodiments, the sample is obtained between 119 and 152 days of gestational age. In some embodiments, the sample is obtained between 138 and 172 days of gestational age. In further embodiments, the sample is obtained between 156 and 196 days of gestational age.
- measurable features can further include clinical variables including, for example, maternal characteristics, age, race, ethnicity, medical history, past pregnancy history, obstetrical history.
- a measurable feature can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero
- diethylstilbestrol exposure multiple gestations, infant sex, short stature, low prepregnancy weight/low body mass index, diabetes, hypertension, urogenital infections, hypothyroidism, asthma, low educational attainment, cigarette smoking, drug use and alcohol consumption.
- the methods of the invention comprise calculation of body mass index (BMI).
- the term“risk score” refers to a score that can be assigned based on comparing the amount of one or more biomarkers or reversal values in a biological sample obtained from a pregnant female to a standard or reference score that represents an average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. Alternatively, the calculated“risk score” can be compared to the average population risk (prevalence of the outcomes). As will be apparent to one of skill in the art, a risk score can represent the positive predictive value (PPV) of the pregnant female’s one or more biomarkers or reversal values for occurrence of the event, i.e., placental dysfunction.
- PPV positive predictive value
- a risk score can also represent the probability of occurrence of the event given the pregnant female’s one or more biomarkers or reversal values.
- the pregnant female’s risk prior to measurement of biomarkers is assigned to be the average population risk (prevalence of the event). Her risk is updated upon measurement of biomarkers and to a post-test risk by calculation of the risk score.
- An individual pre-test risk can also be assigned to a pregnant female based her standard clinical and demographic data, or on individual, family or ancestral health history or genetic data. For example, a pregnant female with a history of prior preeclampsia may have a greater individual risk for placental dysfunction than the average population risk.
- the calculated risk based on biomarkers can then be an updated (post test) risk for the current pregnancy, beyond that individual pre-test risk.
- a calculated risk of placental dysfunction can also be updated by events or information gathered after the test is applied in the current pregnancy. For example, a pregnant female with a calculated risk of placental dysfunction of 30%, but exhibiting later signs or symptoms (e.g., moderately elevated blood pressure) may have an even higher risk of placental dysfunction (>30%) given the combination of the test and the later sign or symptom.
- the risk score is expressed as the log of the reversal value, i.e. the ratio of the relative intensities of the individual biomarkers.
- a risk score can be expressed based on a various data transformations as well as being expressed as the ratio itself. Furthermore, with particular regard to reversal pairs, one skilled in the art will appreciate that any ratio is equally informative if the biomarkers in the numerator and denominator are switched or that related data transformations (e.g., subtraction) are applied. Because the level of a biomarker may not be static throughout pregnancy, a standard or reference score has to have been obtained for the gestational time point that corresponds to that of the pregnant female at the time the sample was taken. The standard or reference score can be predetermined and built into a predictor model such that the comparison is indirect rather than actually performed every time the probability is determined for a subject.
- a risk score can be a standard (e.g, a number) or a threshold (e.g, a line on a graph).
- the value of the risk score correlates to the deviation, upwards or downwards, from the average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females.
- the methods of the invention can be practiced with samples obtained from pregnant females with a specified BMI.
- BMI is an individual’s weight in kilograms divided by the square of height in meters.
- BMI does not measure body fat directly, but research has shown that BMI is correlated with more direct measures of body fat obtained from skinfold thickness measurements, bioelectrical impedance, densitometry (underwater weighing), dual energy x-ray absorptiometry (DXA) and other methods.
- DXA dual energy x-ray absorptiometry
- BMI appears to be as strongly correlated with various metabolic and disease outcome as are these more direct measures of body fatness.
- an individual with a BMI below 18.5 is considered underweight
- an individual with a BMI of equal or greater than 25.0 to 29.9 is considered overweight
- an individual with a BMI of equal or greater than 30.0 is considered obese.
- the predictive performance of the claimed methods can be improved with a BMI stratification of equal or greater than 18, equal or greater than 19, equal or greater than 20, equal or greater than 21, equal or greater than 22, equal or greater than 23, equal or greater than 24, equal or greater than 25, equal or greater than 26, equal or greater than 27, equal or greater than 28, equal or greater than 29 or equal or greater than 30.
- the predictive performance of the claimed methods can be improved with a BMI stratification of equal or less than 18, equal or less than 19, equal or less than 20, equal or less than 21, equal or less than 22, equal or less than 23, equal or less than 24, equal or less than 25, equal or less than 26, equal or less than 27, equal or less than 28, equal or less than 29 or equal or less than 30.
- biological sample encompasses any sample that is taken from pregnant female and contains one or more of the biomarkers disclosed herein.
- suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine.
- the biological sample is selected from the group consisting of whole blood, plasma, and serum.
- the biological sample is serum.
- a biological sample can include any fraction or component of blood, without limitation, T cells, monocytes, neutrophils, erythrocytes, platelets and microvesicles such as exosomes and exosome-like vesicles.
- the biological sample is serum.
- the term“amount” or“level” as used herein refers to a quantity of a biomarker that is detectable or measurable in a biological sample and/or control.
- the quantity of a biomarker can be, for example, the quantity of nucleic acid (i.e. miRNA), the quantity of a polypeptide, the quantity of nucleic acid, or the quantity of a fragment or surrogate.
- the term can alternatively include combinations thereof.
- the term“amount” or“level” of a biomarker is a measurable feature of that biomarker.
- nucleic acid amplification methods can be used to detect a polynucleotide biomarker.
- the oligonucleotide primers and probes of the present invention can be used in amplification and detection methods that use nucleic acid substrates isolated by any of a variety of well-known and established methodologies (e.g., Sambrook et al ., Molecular Cloning, A laboratory Manual, pp. 7.37-7.57 (2nd ed., 1989); Lin et al. , in Diagnostic Molecular Microbiology, Principles and Applications, pp. 605-16 (Persing et al, eds.
- Methods for amplifying nucleic acids include, but are not limited to, for example the polymerase chain reaction (PCR) and reverse transcription PCR (RT-PCR) (see e.g., U.S. Pat. Nos. 4,683,195; 4,683,202; 4,800,159; 4,965,188), ligase chain reaction (LCR) (see, e.g., Weiss, Science
- PCR polymerase chain reaction
- RT-PCR reverse transcription PCR
- LCR ligase chain reaction
- SDA strand displacement amplification
- tSDA Thermophilic SDA
- European Pat. No. 0 684 315 European Pat. No. 0 684 315
- digital PCR see, e.g., Salipante et al., Clin. Chem. doi: 10.1373/clinchem.2019.304048 (2019)
- U.S. Pat. No. 5,130,238 Lizardi et al, BioTechnol. 6: 1197-1202 (1988); Kwoh et al, Proc. Natl. Acad. Sci. USA 86: 1173-77 (1989); Guatelli et al, Proc. Natl. Acad. Sci. USA 87: 1874-78 (1990); U.S. Pat. Nos. 5,480,784; 5,399,491; US Publication No. 2006/46265.
- measuring mRNA in a biological sample can be used as a surrogate for detection of the level of the corresponding protein biomarker in a biological sample.
- any of the biomarkers, biomarker pairs or biomarker reversal panels described herein can also be detected by detecting the appropriate RNA.
- Levels of mRNA can measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR).
- RT-PCR is used to create a cDNA from the mRNA.
- the cDNA can be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell.
- Digital PCR is a special case of qPCR, where PCR is performed in many discrete partitions of the sample, and can be more sensitive and reliable than traditional qPCR (see, e.g., Salipante et al, Clin. Chem. doi: 10.1373/clinchem.2019.304048 (2019)).
- Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols. Richard A. Shimkets, editor, Humana Press, 2004.
- the invention provides a panel of isolated nucleic acid biomarkers comprising two or more of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a panel of isolated nucleic acid biomarkers comprising two or more of the nucleic acid biomarkers set forth in Tables 3-6, 15, 17 or 18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-6, 15, 17 or 18.
- the invention provides a biomarker panel comprising two or more of the isolated nucleic acid biomarkers selected from the group consisting of hsa-miR- 423-3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-4732-5p, hsa-miR- 1273h-3p and hsa-miR-941.
- the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa-miR-33 l-3p and/or hsa-miR-941.
- the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa- miR-423-3p, hsa-miR-516b-5p, hsa-miR-4732-5p, and/or hsa-miR-1273h-3p. In some embodiments, the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa- miR-4732-5p, hsa-miR-516b-5p, and/or hsa-miR-941.
- the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa-miR-155-5p, hsa- miR-33 l-3p, hsa-miR-1273h-3p, and/or hsa-miR-516b-5p. In some embodiments, the biomarker panel comprises isolated nucleic acid biomarkers comprising hsa-miR-516b-5p, and/or hsa-miR- 941.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers selected from the group consisting of hsa-miR-423- 3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331 -3 p, hsa-miR-4732-5p, hsa-miR-1273h-3p and hsa-miR-941.
- the nucleic acid biomarkers selected from the group consisting of hsa-miR-423- 3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331 -3 p, hsa-miR-4732-5p, hsa-miR-1273h-3p
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-331-3 p and/or hsa-miR-941.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-4732-5p, and/or hsa-miR-1273h-3p.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-4732-5p, hsa-miR-516b-5p, and/or hsa-miR-941.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to nucleic acid biomarkers hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-1273h-3p, and/or hsa-miR-516b-5p.
- the composition comprises isolated nucleic acid biomarkers comprising hsa- miR-516b-5p, and/or hsa-miR-941.
- the invention provides a pair of biomarker selected from the group consisting of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18.
- the invention provides a pair of nucleic acid biomarkers selected from the group consisting of the biomarker pairs set forth in Tables 7-10 or 16-18.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of the biomarker pairs set forth in Tables 7-10 or 16-18.
- the invention provides a pair of nucleic acid biomarkers, or a panel of isolated nucleic acid biomarkers comprising a pair of biomarkers, where the pair of biomarkers is selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR- 4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa- miR-155-5p, hsa-let-7i-5p/hsa-miR
- the pair of biomarkers is selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR- 4732-3p/hsa-miR-941, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR- 150-3p/hsa-miR-193b-5p, hsa-miR- 1285-3p/hsa-mir-378c, hsa-miR-4732-3p/hsa-miR- 381 -3p, hsa-miR-320b/hsa-miR-155-5p, hsa-miR-181a-5p/hsa-miR-155-5p, hsa-miR-miR-
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa- miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173- 5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa- miR-155-5p, hsa-let-7i-5p/hsa-
- the pair of biomarkers is selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR- 4732-3p/hsa-miR-941, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR- 150-3p/hsa-miR-193b-5p, hsa-miR- 1285-3p/hsa-mir-378c, hsa-miR-4732-3p/hsa-miR- 381 -3p, hsa-miR-320b/hsa-miR-155-5p, hsa-miR-181a-5p/hsa-miR-155-5p, hsa-miR-miR-
- a pair of nucleic acid biomarkers is selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98- 5p/hsa-miR-485-5p, hsa-miR- 182-5p/hsa-miR-485-5p, and hsa-miR-150-3p/hsa-miR-193b-5p.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa- miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR- 182-5p/hsa-miR-485-5p, and hsa-miR- 150-3p/hsa-miR-193b-5p.
- a pair of nucleic acid biomarkers consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa- miR-485-5p, hsa-m
- the invention provides a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa- miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7- 5p/hsa-miR-941, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-345-5p/hsa-miR-324-3p.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR- 3173-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-345-5p/hsa- miR-324-3p.
- a pair of biomarker selected from the group consisting of hsa-miR-4732
- the invention provides a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa- miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7- 5p/hsa-miR-941, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-345-5p/hsa-miR-324-3p.
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR- 3173-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-345-5p/hsa- miR-324-3p.
- a pair of biomarker selected from the group consisting of hsa-miR-4732
- the invention provides a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa- miR-155-5p, hsa-let-7i-5p/hsa-miR-155-5p, hsa-let-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa- miR-155-5p, hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98- 5p/hsa-miR-485-5p, hsa-miR-182-5p/hsa-miR-485-5p, hsa-
- the invention provides a composition of labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of biomarker selected from the group consisting of hsa-miR-451a/hsa-miR-155-5p, hsa- miR-125a-5p/hsa-miR-155-5p, hsa-let-7i-5p/hsa-miR-155-5p, hsa-let-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa-miR-155-5p, hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR- 485-5p, hsa-miR-98-5p/hsa-miR
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of two or more of the nucleic acid biomarkers listed in Tables 3-11 or 15-18 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-11 or 15-18 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of two or more of the nucleic acid biomarkers listed in Tables 3-6, 15, 17 or 18 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers set forth in Tables 3-6, 15, 17 or 18 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of two or more of the nucleic acid biomarkers selected from the group consisting of hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-4732-5p, hsa-miR-1273h-3p and hsa-miR-941 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the nucleic acid biomarkers selected from the group consisting of hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to two or more of the nucleic acid biomarkers selected from the group consisting of hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR- 4732-5p, hsa-miR-1273h-3p and hsa-miR-941 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-331-3p and/or hsa-miR-941 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-331-3p and/or hsa-miR-941 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules;
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-4732-5p, and/or hsa-miR-1273h-3p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-423-3p, hsa-miR-516b-5p, hsa-miR-4732-5p, and/or hsa- miR-1273h-3p in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules,; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- labeled and/or amplified nucleic acid molecules for example, amplified labeled nucleic
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-4732-5p, hsa-miR-516b-5p, and/or hsa-miR-941 in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- hsa-miR-516b-5p, and/or hsa-miR-941 is measured.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-4732-5p, hsa-miR-516b-5p, and/or hsa-miR-941 in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- labeled and/or amplified nucleic acid molecules for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-47
- the labeled and/or amplified nucleic acid molecules correspond to hsa-miR-516b-5p, and/or hsa-miR-941 is measured.
- Also provided by the invention is a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-1273h-3p, and/or hsa-miR-516b-5p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to hsa-miR-155-5p, hsa-miR-331-3p, hsa-miR-1273h-3p, and/or hsa- miR-516b-5p in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules; calculating a risk score based upon the measured levels of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, to determine the pregnant female’s risk of developing placental dysfunction.
- labeled and/or amplified nucleic acid molecules for example, amplified labeled nucleic acid molecules
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-451a/hsa- miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa4et-7i-5p/hsa-miR-
- the pair of biomarkers is selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa-miR-941, hsa- miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR- 150-3 p/hsa-miR- 193b-5p, hsa-miR-1285-3p/hsa-mir-378c, hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR- 320b/hsa-miR- 155-5p, hsa-miR- 181 a-5p/hsa-miR- 155-5p, hsa-miR- 18
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa-miR-381-3p, hsa- miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR- 3173-5p, hsa-miR-451a/hsa-miR-155-5p, hsa-miR- 125a-5p/h
- the pair of biomarkers is selected from the group consisting of hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR- 1273h-3p/hsa- miR-3173-5p, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-150-3p/hsa-miR-193b-5p, hsa-miR- 1285-3p/hsa-mir-378c, hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-320b/hsa-miR-155-5p, hsa- miR-181a-5p/hsa-miR-155-5p, hsa-miR-26b-5
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b- 5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR-182-5p/hsa-miR-485-5p, and hsa- miR-150-3p/hsa-miR-193b-5p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers hsa-miR- 127-3 p/hsa- miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR-182- 5p/hsa-miR-485-5p, and hsa-miR- 150-3p/hsa-miR-193b-5p in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa-miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR-378e/hsa-miR-221- 5p, and hsa-miR-345-5p/hsa-miR-324-3p in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-4732-3p/hsa-miR-381-3p, hsa-miR-4732-3p/hsa-miR-941, hsa- miR-155-5p/hsa-miR-3173-5p, hsa-miR-1273h-3p/hsa-miR-3173-5p, hsa-miR-7-5p/hsa-miR- 941, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-345-5p/hsa-m
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR-7-5p/hsa-miR-941, hsa-miR- 150-3p/hsa-miR-193b-5p, hsa-miR-378e/hsa-miR-221-5p, and hsa-miR-1285-3p/hsa-mir-378c in a biological sample obtained from the pregnant female, and calculating a risk score based upon the measured amounts of the nucleic acid biomarkers to determine the pregnant female’s risk of developing placental dysfunction.
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p, hsa-miR- 7-5p/hsa-miR-941, hsa-miR-150-3p/hsa-miR-193b-5p, hsa-miR-378e/hsa-miR-221-5p, and hsa- miR-1285-3p/hsa-mir-378c in a biological sample obtained from the pregnant female; measuring the levels of expression of the labeled
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising measuring the amount of a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR- 451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa-let-7i-5p/hsa-miR-155-5p, hsa- let-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa-miR-155-5p, hsa-miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR-485-5p, hsa-miR-98-5p/hsa-miR-485-5p
- the invention provides a method of determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy comprising producing labeled and/or amplified nucleic acid molecules, for example, amplified labeled nucleic acid molecules, that correspond to a pair of nucleic acid biomarkers selected from the group consisting of hsa-miR-451a/hsa-miR-155-5p, hsa-miR-125a-5p/hsa-miR-155-5p, hsa-let- 7i-5p/hsa-miR-155-5p, hsa-let-7b-5p/hsa-miR-155-5p, hsa-miR-25-3p/hsa-miR-155-5p, hsa- miR-127-3p/hsa-miR-485-5p, hsa-miR-26b-5p/hsa-miR
- the quantitation of biomarkers in a biological sample can be determined, without limitation, by the methods described above as well as any other method known in the art.
- the quantitative data thus obtained is then subjected to an analytic classification process.
- the raw data is manipulated according to an algorithm, where the algorithm has been pre-defmed by a training set of data, for example as described in the examples provided herein.
- An algorithm can utilize the training set of data provided herein, or can utilize the guidelines provided herein to generate an algorithm with a different set of data.
- An analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample.
- Examples of useful methods include linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, machine learning algorithms; etc.
- Classification can be made according to predictive modeling methods that set a threshold for determining the probability that a sample belongs to a given class.
- the probability preferably is at least 50%, or at least 60%, or at least 70%, or at least 80% or higher.
- Classifications also can be made by determining whether a comparison between an obtained dataset and a reference dataset yields a statistically significant difference. If so, then the sample from which the dataset was obtained is classified as not belonging to the reference dataset class. Conversely, if such a comparison is not statistically significantly different from the reference dataset, then the sample from which the dataset was obtained is classified as belonging to the reference dataset class.
- a desired quality threshold is a predictive model that will classify a sample with an accuracy of at least about 0.5, at least about 0.55, at least about 0.6, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or higher.
- a desired quality threshold can refer to a predictive model that will classify a sample with an AUC of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.
- the relative sensitivity and specificity of a predictive model can be adjusted to favor either the selectivity metric or the sensitivity metric, where the two metrics have an inverse relationship.
- the limits in a model as described above can be adjusted to provide a selected sensitivity or specificity level, depending on the particular requirements of the test being performed.
- One or both of sensitivity and specificity can be at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.
- the raw data can be initially analyzed by measuring the values for each biomarker, usually in triplicate or in multiple triplicates.
- the data can be manipulated, for example, raw data can be transformed using standard curves, and the average of triplicate measurements used to calculate the average and standard deviation for each patient. These values can be transformed before being used in the models, e.g. log-transformed, Box-Cox transformed (Box and Cox, Royal Stat. Soc.. Series B, 26:211-246(1964).
- the data are then input into a predictive model, which will classify the sample according to the state.
- the resulting information can be communicated to a patient or health care provider.
- a robust data set comprising known control samples and samples corresponding to the birth classification of interest is used in a training set.
- a sample size can be selected using generally accepted criteria. As discussed above, different statistical methods can be used to obtain a highly accurate predictive model.
- hierarchical clustering is performed in the derivation of a predictive model, where the Pearson correlation is employed as the clustering metric.
- One approach is to consider a given birth dataset as a“learning sample” in a problem of“supervised learning.”
- CART is a standard in applications to medicine (Singer, Recursive Partitioning in the Health Sciences. Springer(1999)) and can be modified by transforming any qualitative features to quantitative features; sorting them by attained significance levels, evaluated by sample reuse methods for Hotelling’s T 2 statistic; and suitable application of the lasso method.
- Problems in prediction are turned into problems in regression without losing sight of prediction, indeed by making suitable use of the Gini criterion for classification in evaluating the quality of regressions.
- Logic regression resembles CART in that its classifier can be displayed as a binary tree. It is different in that each node has Boolean statements about features that are more general than the simple“and” statements produced by CART.
- the false discovery rate can be determined.
- a set of null distributions of dissimilarity values is generated.
- the values of observed profiles are permuted to create a sequence of distributions of correlation coefficients obtained out of chance, thereby creating an appropriate set of null distributions of correlation coefficients (Tusher et al, Proc. Natl. Acad. Sci. U.S.A 98, 5116-21 (2001)).
- the set of null distribution is obtained by: permuting the values of each profile for all available profiles; calculating the pair-wise correlation coefficients for all profile; calculating the probability density function of the correlation coefficients for this permutation; and repeating the procedure for N times, where N is a large number, usually 300.
- N is a large number, usually 300.
- the FDR is the ratio of the number of the expected falsely significant correlations (estimated from the correlations greater than this selected Pearson correlation in the set of randomized data) to the number of correlations greater than this selected Pearson correlation in the empirical data (significant correlations).
- This cut-off correlation value can be applied to the correlations between experimental profiles. Using the aforementioned distribution, a level of confidence is chosen for significance. This is used to determine the lowest value of the correlation coefficient that exceeds the result that would have obtained by chance.
- this method one obtains thresholds for positive correlation, negative correlation or both. Using this threshold(s), the user can filter the observed values of the pair wise correlation coefficients and eliminate those that do not exceed the threshold(s).
- an estimate of the false positive rate can be obtained for a given threshold. For each of the individual“random correlation” distributions, one can find how many observations fall outside the threshold range. This procedure provides a sequence of counts. The mean and the standard deviation of the sequence provide the average number of potential false positives and its standard deviation.
- variables chosen in the cross-sectional analysis are separately employed as predictors in a time-to-event analysis (survival analysis), where the event is the occurrence of preterm birth, and subjects with no event are considered censored at the time of giving birth.
- a parametric approach to analyzing survival can be better than the widely applied semi- parametric Cox model.
- a Weibull parametric fit of survival permits the hazard rate to be monotonically increasing, decreasing, or constant, and also has a proportional hazards representation (as does the Cox model) and an accelerated failure-time representation. All the standard tools available in obtaining approximate maximum likelihood estimators of regression coefficients and corresponding functions are available with this model.
- Cox models can be used, especially since reductions of numbers of covariates to manageable size with the lasso will significantly simplify the analysis, allowing the possibility of a nonparametric or semi-parametric approach to prediction of time to preterm birth.
- These statistical tools are known in the art and applicable to all manner of proteomic data.
- a set of biomarker, clinical and genetic data that can be easily determined, and that is highly informative regarding the probability for preterm birth and predicted time to a preterm birth event in said pregnant female is provided.
- algorithms provide information regarding the probability for preterm birth in the pregnant female.
- Survival analyses are commonly used to understand time to occurrence of an event of interest such as birth or death.
- the Kaplan-Meier estimator is used to estimate the survival function
- Cox proportional hazards models are used to estimate the effects of covariates on the hazard of event occurrence.
- These models conventionally assume that survival time is based on risk of exactly one type of event. However a competing risk for a different event may be present that either hinders the observation of an event of interest or modifies the chance that this event occurs. Conventional methods may be inappropriate in the presence of competing risks.
- Alternative methods appropriate for analysis of competing risks either asses competing hazards in sub distribution hazards models or cause-specific modified Cox proportional hazards models; or estimate cumulative incidence over competing events (Jason P. Fine & Robert J.
- a subset of markers i.e. at least 3, at least 4, at least 5, at least 6, up to the complete set of markers.
- a subset of markers will be chosen that provides for the needs of the quantitative sample analysis, e.g. availability of reagents, convenience of quantitation, etc., while maintaining a highly accurate predictive model.
- the selection of a number of informative markers for building classification models requires the definition of a performance metric and a user-defined threshold for producing a model with useful predictive ability based on this metric.
- the performance metric can be the AUC, the sensitivity and/or specificity of the prediction as well as the overall accuracy of the prediction model.
- an analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample.
- useful methods include, without limitation, linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, and machine learning algorithms.
- Various methods are used in a training model. The selection of a subset of markers can be for a forward selection or a backward selection of a marker subset. The number of markers can be selected that will optimize the performance of a model without the use of all the markers.
- One way to define the optimum number of terms is to choose the number of terms that produce a model with desired predictive ability (e.g . an AUOO.75, or equivalent measures of sensitivity/specificity) that lies no more than one standard error from the maximum value obtained for this metric using any combination and number of terms used for the given algorithm.
- desired predictive ability e.g . an AUOO.75, or equivalent measures of sensitivity/specificity
- biomarkers of the invention which have been identified in the invention disclosed herein as being useful for predicting placental dysfunction, are known in the art and are readily available in public databases.
- the human microRNAs disclosed herein as biomarkers useful for determining a pregnant female’s risk of developing placental dysfunction are available in mirBase (mirbase.org).
- mirBase mirBase
- the naming convention for microRNAs generally uses in the name of the microRNA, for example, hsa-miR-423-3p.
- Biomarker pairs are generally denoted herein as a pair separated by a‘7”, for example, hsa-miR-127-3p/hsa-miR-485-5p.
- kits for determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy can include one or more agents for detection of biomarkers, a container for holding a biological sample isolated from a pregnant female; and printed instructions for reacting agents with the biological sample or a portion of the biological sample to detect the presence or amount of the isolated biomarkers in the biological sample.
- the agents can be packaged in separate containers.
- the kit can further comprise one or more control reference samples and reagents for performing an immunoassay.
- the kit can comprise one or more containers for compositions or reagents contained in the kit.
- Compositions can be in liquid form or can be lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic.
- the kit can also comprise a package insert containing written instructions for methods for determining a pregnant female’s risk of developing placental dysfunction later in the pregnancy.
- Example 1 Identification of extracellular micro RNA biomarkers for Identification of Pregnancies at Risk for Placental Dysfunction
- This example shows identification of extracellular miRNA biomarkers for prediction of preeclampsia: placental dysfunction affecting maternal blood pressure and renal, liver and central nervous system function.
- Unblinded samples were reserved for the training set. Blinded samples were split between training and verification sets. Additional blinded samples are reserved for future re- verification or validation.
- Maternal serum samples were collected from high-risk and average-risk pregnant women between 17-28 weeks, for which pregnancy outcomes are known. The samples were divided into a training set of 141 subjects (49 Preeclampsia/ 92 Normal) and a verification set of 71 subjects (24 Preeclampsia/ 47 Normal). GABD for the training and test groups was at a minimum of 120 days to a maximum of 201 days with a mean of 163.6 days.
- Total extracellular RNA (exRNA) from each sample was purified and subjected to small RNA sequencing.
- Total extracellular RNA was purified from 500 pL serum using the miRNeasy micro kit (Qiagen), followed by the RNA Clean and Concentrator Kit (Zymo), with a final elution volume of 12 pL.
- 1.2 pL of the resulting exRNA was used to prepare the Small RNAseq libraries using the NEBNext Small RNA Sequencing Library Preparation Kit, using the manufacturer’s instruction except for the following: 1. Adapters were diluted 1 :6; 2. The reactions were run at 1/5* 11 volume using a mosquito HTS liquid handler.
- Up to 384 libraries were prepared in a given batch (some of the libraries for this project were prepared in the same batches as other projects), and libraries were multiplexed using the available 48 NEB Small RNAseq indices. Up to 48 samples were combined per pool, and each pool was size selected using a Pippin Prep (either 177-180 bp, or 125-160 bp). Samples were sequenced on a HiSeq 4000. Each pool of up to 48 samples was loaded onto its own lane, generating at least 350 million single-end 75 bp reads.
- Unblinded UCSD samples were reserved for the training set. Blinded Sera samples were split between training and verification sets, requiring balance between the training and test sets of gestational age at blood draw (GABD), and of the proportions of preeclampsia cases to non-preeclamptic controls across all GABD and in 1- and 3-week windows of GABD.
- GABD gestational age at blood draw
- Univariate miRNA models were fit to the entire dataset range of gestational age at blood draw (GABD) and to early, middle and late GABD windows. Univariate models with significant chi-square p-values between residual and null deviance were selected in each window.
- Bivariate reversals were selected by ranking performance in bootstrapped resampling with replacement.
- hypotheses will be tested en masse for AUC > 0.5 (95% Cl does not include 0.5) in the Verification phase. Surviving hypotheses will be filtered for overall performance in both the Discovery and Verification sets and for kinetics of performance in these sets.
- Top ranking reversals after Verification testing and filtering may be:
- hsa.miR.375 39 0.697 0.661 0.100 0.060 0.306 hsa.miR.221.3p / hsa.miR.24.2.5p 40 0.699 0.660 0.092 0.047 0.614 hsa.miR.374b.5p / hsa.miR.342.3p 41 0.709 0.669 0.119 0.078 0.140 hsa.miR.125a.3p / hsa.miR.589.5p 42 0.699 0.664 0.090 0.066 0.238 hsa.miR.4732.5p / hsa.miR.516b.5p 43 0.744 0.703 0.183 0.129 0.087 hsa.miR.4732.5p / hsa.miR.23a.3p 44 0.726 0.675 0.233 0.175 0.093 hsa.miR.374b
- miR.130b.5p 34 0.692 0.654 0.048 0.035 2.000 hsa. miR.4443 / hsa. miR.130b.5p 35 0.688 0.653 0.048 0.033 2.620 hsa. miR.181a.5p / hsa. miR.130b.5p 36 0.689 0.649 0.050 0.035 1.779 hsa. miR.1323 / hsa. miR.485.5p 37 0.706 0.668 0.091 0.052 0.448 hsa. miR.126.3p / hsa. miR.155.5p 38 0.701 0.662 0.060 0.035 0.587 hsa.
- miR.485.5p 43 0.694 0.657 0.070 0.026 0.782 hsa. miR.320a / hsa. miR.485.5p 44 0.674 0.641 0.072 0.034 0.782 hsa. miR.451a / hsa. miR.485.5p 45 0.688 0.650 0.071 0.027 0.767 hsa.mir.320a / hsa. miR.485.5p 46 0.699 0.662 0.094 0.062 0.446 hsa. miR.185.5p / hsa. miR.485.5p 47 0.680 0.642 0.071 0.034 0.672 hsa.
- Placental dysfunction for which the most common clinical manifestations are preeclampsia (PE) and intrauterine growth restriction (IUGR), is an important cause of fetal and maternal morbidity and mortality. Affecting approximately 5% of pregnancies (Bartsch et al., BMJ 35341753 (2016)), PE is the second leading cause of maternal mortality (Ness, Am J Obstet Gynecol 175(5): 1365-1370 (1996); Sibai, Ob stet Gynecol 102(1): 181-92 (2003)) and the leading cause of medically indicated preterm birth (miPTB) in the US, accounting for 15% of all PTBs (Sibai, Semin Perinatol 30(1): 16-19 (2006)).
- PE is typically diagnosed by a combination of new-onset hypertension and proteinuria, but severe cases can be associated with maternal end organ damage, including cerebral edema, pulmonary edema, liver or kidney failure, hemolysis, or thrombocytopenia, placental abruption, seizures (eclampsia), or maternal and fetal death.
- the clinical manifestations of PE become apparent in the second half of pregnancy, but they arise from dysregulation of feto-placental development and/or maternal adaptation to pregnancy in early pregnancy.
- the highest performing first trimester risk assessment algorithm was based on a multivariate model incorporating a variety of maternal characteristics (e.g., maternal age, weight, height, race, smoking, assisted reproductive technologies, prior pregnancy with preeclampsia or small for gestational age ( ⁇ 10th percentile, SGA), chronic hypertension, diabetes mellitus, lupus, antiphospholipid syndrome, and family history of preeclampsia.), serum analyte values** (e.g., pregnancy-associated plasma protein A (PAPP-A) and Placental Growth Factor (PLGF)), mean arterial pressure, and uterine artery pulsatility index, and reported detection rates of 95.3% for early ( ⁇ 34 week) PE, 45.6% for late PE, 55.5% for preterm SGA, and 44.3% for term SGA with a false positive rate (FPR) of 10% (Poon et al., Fetal Diagn Ther 33(1): 16-27 (2013)).
- maternal characteristics e.g., maternal age
- VEGF Vascular endothelial growth factor
- sFlt-1 soluble fms-like tyrosine kinase 1
- PLGF levels have shown promise as predictive biomarkers in the third trimester, primarily due to their high negative predictive value (Levine et al., New England Journal of Medicine 350(7):672-683 (2004); Tjwa et al., Cell and Tissue Research 314(1):5-14 (2003); Caillon et al., Ann Lab Med 38(2):95- 101 (2018)).
- RNAs extracellular RNAs in a variety of biofluids have been shown to have potential value as diagnostic and prognostic biomarkers for a variety of conditions, including cancer, heart disease, neurodegenerative disease, and liver injury (reviewed in (Das et al., Cell 177(2):231-242 (2019)).
- the Discovery cohort samples 28 cases and 26 controls were collected after diagnosis and analyzed by small RNAseq and the Verification cohort samples (only 6 cases and 10 controls) were collected pre- symptomatically and analyzed by qRTPCR (Timofeeva et al., Placenta 61 :61-71 (2016)).
- RNA-seq of maternal serum exRNAs was performed to discover and verify miRNAs differentially expressed in patients who later developed preeclampsia.
- Discovery and verification of univariate and bivariate miRNA biomarkers revealed that bivariate biomarkers verified at a markedly higher rate than univariate biomarkers.
- the majority of verified biomarkers contained miR-155-5p, which has been reported to mediate the preeclampsia-associated repression of eNOS by TNFa.
- Deconvolution analysis revealed that several verified miRNA biomarkers came from the placenta and were likely carried by placenta-specific extracellular vesicles. Both univariate extracellular miRNAs biomarkers and bivariate reversals were discovered and verified that identified asymptomatic patients at elevated risk for later development of preeclampsia. The verification rate for reversals was markedly higher than for univariate biomarkers, indicating that the use of reversals can confer a degree of internal normalization that increases robustness.
- Maternal serum was collected between 17-28 weeks gestation. Samples were obtained from the high-risk Placental Study at the University of California, San Diego and from the average-risk Proteomic Assessment of Preterm Risk (PAPR) Study at Sera Prognostics. Eligibility criteria for the two studies are listed in Tables 12A and 12B.
- Eligibility criteria for the UCSD Placenta Study included: abnormal first or second trimester analytes defined by PAPP-A ⁇ 0.3 multiples of the medium (MoM), alpha fetoprotein (AFP) >2.5 MoM, Inhibin > 2.0 MoM, and Estradiol ⁇ 0.30 MoM and/or prior adverse pregnancy outcome attributable to preeclampsia and/or maternal co-morbidities associated with increased risk for preeclampsia.
- MoM medium
- AFP alpha fetoprotein
- Inhibin > 2.0 MoM
- Estradiol ⁇ 0.30 MoM and/or prior adverse pregnancy outcome attributable to preeclampsia and/or maternal co-morbidities associated with increased risk for preeclampsia.
- Antibodies used in the studies include anti-CD63 antibody (BD Pharmingen; San Jose, CA), anti-AG02 Antibody (Abeam; Cambridge, UK), anti-PLAP Antibody (Abeam), anti- CD63 Antibody (BD Pharmingen), anti-AG02 Antibody (Abeam), and anti-PLAP Antibody (Abeam).
- Commercial assays used in the studies include miRNeasy micro kit (Qiagen;
- Deposited data includes small RNA-seq data and miRNA.
- Software and algorithms used include exceRpt small RNA-seq pipeline for exRNA profiling (Genboree Bioinformatics, genboree.org/java-bin/login.jsp).
- Maternal serum [00215] Maternal blood was collected by peripheral venipuncture into BD Vacutainer serum blood collection tubes (Becton Dickinson; Franklin Lakes, NJ), held at room temperature for at least 10 minutes and centrifuged at 2000 x g for 10 minutes. The serum was divided into 1 mL aliquots and stored at -80°C until RNA extraction was performed.
- Placenta tissue samples ( ⁇ 0.5 cm x 0.5 cm x 0.5 cm) were collected after elective termination procedures (5-22 weeks gestational age) or delivery (22-42 weeks), and immediately placed in RNAlater (ThermoFisher). After storage in RNAlater for 24 hours-7 days, the tissue samples were transferred into clean microfuge tubes and stored at -80°C until RNA extraction.
- RNAlater (ThermoFisher). After storage in RNAlater for 24 hours-7 days, the tissue samples were transferred into clean microfuge tubes and stored at -80°C until RNA extraction.
- PBMC peripheral blood mononuclear cells
- RBC washed red blood cells
- the CTAD tubes were centrifuged at 100 x g for 20 minutes with no brake and all but ⁇ 100pL of the supernatant was added to a fresh 15mL conical centrifuge tube.
- Freshly prepared Prostaglandin 12 (PGI2) (Abeam) was added to ⁇ 2 mM final concentration.
- the Platelet Rich Plasma (PRP) was then centrifuged at 100 x g for 20 minutes with no brake, and all but -100 pL of the supernatant was added to a fresh 15 mL conical centrifuge tube. To pellet the platelets, this tube was centrifuged at 800 x g for 20 minutes with no brake.
- the platelet pellet was washed without pellet resuspension in lOmL of Platelet Wash Buffer (PWB) (IX wash buffer: lOmM Tris, pH 7.5, 138mM NaCl, 1.8mM CaCh, 0.49mM MgCh, luM PGI2).
- PWB Platelet Wash Buffer
- the material was centrifuged at 800 x g for 10 minutes with no brake and the supernatant material was removed to near completion.
- the platelet pellet was gently resuspended in 2mL of PWB and transferred to a 2 mL centrifuge tube. The mixture was centrifuged at 800 x g for 10 minutes with no brake, and the supernatant material was removed to near completion.
- the platelet pellet was stored at -80°C until processed.
- PBMCs and RBCs were purified from the material remaining after the first CTAD tube centrifugation step.
- the remaining PRP, huffy coat, and a small portion of the RBCs were combined by patient and transferred from the CTAD tubes into a fresh 15 mL tube.
- Sufficient PGI2 was added such that the concentration would be 2 mM when PWB was added to a 10 mL total volume. The material was gently inverted several times to mix and centrifuged at lOOx g for 20 minutes with no brake.
- the supernatant material was removed to near completion and the pellet was mixed in lOmL of RBC lysis buffer (150mM NH4CI, lOmM NaHCCh, 1.27mM EDTA) placed at room temperature for 20 minutes. The material was centrifuged at 500 x g for 5 minutes and the supernatant was discarded. The pellet was washed twice with 10 mL of Dulbecco's phosphate-buffered saline (DPBS) each time and centrifuged as before. The pellet was gently resuspended in 2 mL of DPBS and the material was transferred to a 2 mL centrifuge tube and centrifuged as before.
- DPBS Dulbecco's phosphate-buffered saline
- the cells were sorted using a Becton Dickinson FACSAria IIu cell sorter equipped with five lasers (350nm, 405nm, 488nm, 561nm, and 640nm).
- the cell populations were sorted through a 70 pm nozzle tip at a sheath pressure of 70 psi and a drop drive frequency of 90-95 kHz.
- a highly pure sorting modality (4-way purity sorting for FACS Aria, Masks at 0-32-0) was chosen for cell sorting.
- the flow rate was maintained at an approximate speed of 10,000 events/second. Lymphocytes and monocytes were gated based on forward- scattered light (FSC) and side-scattered light (SSC) FSC/SSC properties.
- FSC forward- scattered light
- SSC side-scattered light
- the FSC values are proportional to the diameter of the interrogated cells, whereas the SSC values provides information about the internal complexity of the interrogated cell or its granularity.
- Sorted cells were collected in 5 ml polypropylene tubes containing 1 ml collection medium (RPMI supplemented with 50% FBS, 100 pg/ml gentamicin, 4 mM L-glutamine, 20 mM HEPES) and stored at -80°C until processed.
- Antibody biotinylation Antibodies raised against CD63, AG02, and PLAP were used. Sodium azide was removed from antibody stocks using the Zeba spin desalting column (7K MWCO, 0.5 ml, Thermo Fisher Scientific). Antibodies were then biotinylated using the EZ- LINKTM Sulfo-NHS-LC-Biotin reagent (ThermoFisher), following manufacturer’s protocol. Briefly, 10 mM biotin solution was prepared by dissolving 1 mg of no-weight Sulfo-NHS-LC- Biotin in 180pL ultrapure water (purified by Milli-Q Biocel System; MilliporeSigma,
- Magnetic bead preparation DYNABEADSTM MYONETM Streptavidin T1
- Immunoprecipitation The immunoprecipitation procedure was performed by incubating the serum with antibody conjugated beads. Briefly, serum from pregnant females was thawed and diluted 1 : 1 with double filtered IX PBS (PierceTM 20X PBS, ThermoFisher). Every l,000pL of serum was invert-mixed with 6 pg biotinylated antibody for 20 min at room temperature (RT) on a HULAMIXER® Sample Mixer (ThermoFisher) at 10 rpm. Then, 390pL of washed Dynabeads was added to the mixture and invert-mixed for 25 min at RT on a Hula mixer at 10 rpm. The mixture was then washed three times with 0.1% BSA and subjected to RNA extraction.
- IX PBS PulierceTM 20X PBS, ThermoFisher
- RNA extraction from Dynabeads was extracted using the miRNeasy mini kit (Qiagen) following manufacturer’s protocol.
- the Dynabeads were subjected to phenol/chloroform extraction step for RNA extraction using QIAZOLTM Lysis Reagent (Qiagen) followed by chloroform.
- the aqueous phase was used as input into the miRNeasy procedure and the RNA was eluted in 14 pL of nuclease-free water.
- the RNA samples were also treated with deoxyribonuclease I (DNase I, Invitrogen).
- RNA 6000 Nano Pico Kit (Agilent Technologies) and the Bioanalyzer 2100 (Agilent Technologies).
- the eluted RNA was dried down using a speedvac, and used as input into the small RNAseq library preparation process. Small RNAseq libraries were generated and size selected as described below.
- Qiagen QIAzol Lysis Reagent
- RNA binding buffer 60 pL of the RNA binding buffer and 90 pL of 100% ethanol was added to 30 pL of RNA, transferred to the Zymo- Spin IC columns and centrifuged at 2000xg for 30 s. The column was washed with 700 pL and 400 pL of RNA wash buffer and centrifuged at full speed for 30 sec and 2 min, respectively.
- RNA was then eluted into a final volume of 9 pL RNase free water.
- the size distribution and quality of the extracted RNA was verified on Agilent RNA 6000 Pico chips using the Agilent 2100 Bioanalyzer instrument.
- RNAseq libraries were prepared from 1.2 pL input RNA using the NEBNext Small RNA Sequencing Library Preparation kit (New England BioLabs), using a mosquito HTS automated nanoliter liquid handler (TTP Labtech). For the automation, the reaction volume was reduced to l/5th of the manufacturer’s recommended volume and the adaptors were diluted to l/6th of the manufacturer’s recommended concentration.
- Libraries were then cleaned and concentrated using the Zymo DNA Clean and Concentrator-5 kit (Zymo Research) with a 25 pL elution volume and quantified using the Quant-iT Picogreen DNA Assay High Sensitivity kit (ThermoFisher).
- the size distributions of the library products were determined using the Agilent High Sensitivity DNA chip on the Agilent 2100 Bioanalyzer instrument.
- the libraries were then pooled (up to 48 samples/pool) based on their concentrations and their size distribution, to obtain similar numbers of miRNA reads among libraries.
- the pooled libraries were then subjected to size selection on the
- RNAseq data from the exRNA samples were processed, including adapter trimming and mapping to miRBase (miRbase v.21) to yield Raw Count data, using the ExceRpt small RNA sequencing data analysis pipeline version 4.6.2 with minimum insert length set at 10 nt and no mismatches permitted on the Genboree workbench
- the Placental Dysfunction Clinic samples were unblinded and included in the Discovery set.
- the Sera samples were initially blinded and were divided between Discovery and Verification sets, in a manner that resulted in a similar distribution of gestational age at blood draw (GABD), and of the proportions of preeclampsia cases to non-preeclamptic controls across all GABD and in 1- and 3-week windows of GABD between the Discovery and Verification sets.
- GABD gestational age at blood draw
- Read counts were log2 transformed. Sample-to- sample normalization was carried out through stabilization of variance and reduction in bias across distributions of read counts. Variance stabilizing transformation and bias reduction are useful for making high- and low- read-count samples and miRs more tractable, as stabilizing variance reduces heteroskedasticity and reducing bias removes sample-wide mean shifts.
- the PEER package (Sanger Institute; Cambridge, UK) was run to reduce batch effects while retaining biological variation (Astrand, Journal of
- AUCs were generated with the pROC package, using the Delong and bootstrap methods to establish the confidence intervals (CIs) (Robin et ah, BMC Bioinformatics 12(1):77 (2011); Stegle et ah, PLOS Computational Biology 6(5):el000770 (2010); Parts et ah, PLOS Genetics 7(l):el001276 (2011)). Analysis was performed using R 3.4.3. Processed miRNA data after normalization and batch correction were tabulated.
- reversal scores 25th, 50th, and 75th percentile reversal scores, as well as the median shift in the reversal score, were determined for each reversal in Discovery and Verification.
- the chromosome on which each miRNA is encoded is indicated.
- miRNA cluster if a given miRNA is located in a miRNA cluster, a “Y” is entered.
- tissue atlas for each miRNA, the estimated percent contribution of each cell and tissue type is listed. The cell or tissue type with the highest percentage, and with
- Placenta and Adult Tissue small RNAseq data [00259] Small RNAseq data from the tissue samples were trimmed and mapped using the exceRpt pipeline (Rozowsky et al., Cell Syst 8(4):352-357 (2019)) version 4.6.2 with minimum insert length set at 15 nt and no mismatches permitted. Adult and placenta cell/tissue miRNA data was collected and deconvolution analysis was performed.
- Scaled data (expressed as reads per million total miRNA reads) (scaled adult and placenta cell/tissue miRNA data were tabulated), scaled expression values averaged for each miRNA, and each cell/tissue type were determined (scaled cell/tissue miRNA data averaged for each cell/tissue type was tabulated)).
- Differential expression analysis using the sample level data was performed using the Multigroup Comparison function in Qlucore (qlucore.com; Lund, Sweden) was tabulated), and data for highly significantly (q-value ⁇ 10-12) differentially expressed miRNAs were tabulated (scaled cell/tissue data for miRNAs highly significantly (q-value ⁇ 10-12) expressed among cell/tissue types).
- the resulting values were scaled across cell/tissue types to compute the percent of that miRNA present in maternal serum that was contributed by each cell/tissue type (calculation of the percent contribution of each cell/tissue type to the level of each miRNA in maternal serum was tabulated).
- RNAseq data from the tissue samples were trimmed and mapped using the exceRpt pipeline (Rozowsky et al., Cell Syst 8(4):352-357 (2019)) version 4.6.2 with minimum insert length set at 15 nt and no mismatches permitted. Scaled data (expressed as reads per million total miRNA reads) were determined. Multigroup differential expression analysis was performed using Qlucore (Qlucore.com) and miRNAs that were significantly differentially expressed (q ⁇ 0.05) between at least 2 groups (input, CD63, AG02, PLAP) were identified.
- Maternal serum samples were collected as part of two studies: the Placenta Study at UCSD, and the PAPR Study from Sera Prognostics.
- the Placenta Study was a single-site high- risk study that enrolled pregnant women with at least one risk factor for placental dysfunction, while the PAPR Study was a multi-site study that enrolled pregnant women without regard to risk factors for placental dysfunction Table 12A.
- subjects were enrolled and maternal serum was collected between 17-28 weeks, and outcomes were obtained after delivery.
- Nineteen cases and 29 controls were selected from the Placenta Study and 54 cases and 110 controls were selected from the PAPR study (selection criteria are listed in Table 12B).
- RNAseq data were mapped using the exceRpt pipeline (Rozowsky et ah, Cell Syst 8(4):352-357 (2019)) and the resulting miRNA data were filtered to remove miRNAs with > 70% missing values.
- Sample-to-sample normalization was carried out through stabilization of variance and reduction in bias across distributions of read counts. Batch normalization was carried out using the PEER package (Astrand, Journal of Computational Biology 10(1):85-102 (2003).
- Table 14 Tally of candidate univariate predictors and reversals selected in discovery and passing verification
- Reversals were selected by ranking performance in bootstrapped resampling with replacement, as detailed as described above. Briefly, five ranks were derived from the following statistics, each computed across 1000 iterations of cross-validation: 1) the mean of the cross- validation AUCs; 2) the lower 25th percentile of the cross-validation AUCs; 3) the mean of the squared Pearson correlation coefficient between the reversal scores with diagnosis of
- Preeclampsia Case (1) or Control (0) 4) the lower 25th percentile of the squared Pearson correlation coefficient between the reversal scores and the diagnosis of Preeclampsia Case (1) or Control (0); and 5) the square of the differences in the case mean and control mean reversal scores (i.e., the squared mean shift). Each rank was then inverted and all ranks were summed for each reversal to obtain the final ranking.
- miR.30d.5p/miR.155.5p miR.151a.3p/miR.155.5p
- let.7g.5p/miR.155.5p let.7i.5p/miR.155.5p
- miR.451a/miR.155.5p miR.126.3p/miR.155.5p
- miR.26a.5p/miR.155.5p miR.30d.5p/miR.155.5p, miR.151a.3p/miR.155.5p, let.7g.5p/miR.155.5p, let.7i.5p/miR.155.5p, miR.451a/miR.155.5p, miR.126.3p/miR.155.5p, miR.26a.5p/miR.155.5p,
- miR.425.5p/miR.155.5p miR.181a.5p/miR.155.5p, miR.363 3p/miR.155.5,
- miR.320a/miR.155.5p miR.320b/miR.155.5p, miR.99a.5p/miR.155.5p,
- miR.125a.5p/miR.155.5p miR.625.3p/miR.155.5p
- miR.146b.5p/miR.155.5p miR.146b.5p/miR.155.5p
- miR.146a.5p/miR.155.5p miR.4443/miR.155.5p
- miR.516b.5p/miR.155.5p miR.4a.5p/miR.155.5p
- miRNAs are shared among multiple reversals or between univariate predictors and reversals
- hsa-miR-485-5p hsa-miR-941, hsa-miR-3173-5p, and hsa-miR-155- 5p
- hsa-miR-155-5p was in the denominator of all 23 of the reversals discovered and verified in the Late GABD window (Late/Late), as well as in the numerator of one of the Early/Early reversals.
- hsa-miR-26b-5p was the numerator in one Late/Late and one Full, Late/Middle reversal.
- Verified predictors include placenta-associated miRNAs
- Verified predictors include members of two placenta-associated miRNA clusters, one located on Chromosome 14 (Seitz et al., Genome Res 14: 1741-1748 (2004)) (Tables 17 and 18, Numerator chromosome, Denominator chromosome, and In miRNA Cluster) and the other on Chromosome 19 (Bentwich et al., Nature Genetics 37:766 (2005)) (Tables 17 and 18, Numerator chromosome, Denominator chromosome, and In miRNA Cluster).
- RNAseq was performed on: Peripheral Blood Mononuclear Cells (PBMCs), Red Blood Cells (RBCs) and platelets collected by centrifugation from human plasma; Granulocytes, Lymphocytes, and Monocytes isolated from human plasma by fluorescence activated cell sorting; and adult human Brain, Heart, Intestine, Kidney, Liver, Lung, and Pancreas, and human Placenta collected from 17-28 weeks gestation (Sample level data: Data averaged for each cell/tissue type). miRNAs that are highly significantly (q-value ⁇ 10-12) differentially expressed among cell/tissue types were identified and combined with the raw exRNA data from the
- Placenta-associated miRNA predictors are associated with CD63+ and PLAP+ carrier subclasses
- EVs extracellular vesicles
- RNPs ribonucleoprotein complexes
- CD63 a commonly used EV surface marker
- PLAP a placental EV- associated surface marker
- AG02 a component of the RNA-induced silencing complex, and associated with a large fraction of the extracellular miRNAs that are not associated with EVs (Turchinovich et ak, Nucleic Acids Res 39(16):7223-7233 (2011)), respectively.
- RNAseq was performed on these immunoaffmity enriched samples, as well as the input pooled serum, and then identified miRNAs that were significantly (q ⁇ 0.05) differentially expressed among these groups.
- Hierarchical clustering allowed identification of eight co-expressed sets of miRNAs, each of which had a characteristic pattern of enrichment in one or more carrier subclass. Briefly, for miRNAs associated with different carrier subclasses, a heatmap was generated showing eight sets of co-expressed miRNAs identified by hierarchical clustering. The three expected sets of miRNAs that showed non-overlapping associations with CD63, AG02, or PLAP indicate that certain miRNAs are loaded into distinct carrier subclasses that display only one of these three markers.
- the two sets of miRNAs that were enriched for two markers suggest that some miRNAs are associated with either two carrier subclasses or with a single carrier subclass displaying both markers.
- the two sets of miRNAs that were strongly detected in both the Input and associated with one of the markers are consistent with certain miRNAs being associated with two carrier subclasses, one displaying either CD63 or AG02 and one that does not display any of the three tested markers.
- the set of miRNAs detected in unfractionated pregnant serum but not associated with any of the three tested markers indicates that there remains one or more other carrier subclasses that do not display any of the three tested markers. Therefore, each of the detected miRNAs was assigned to one of these Carrier Subclass groups: CD63, AG02, PLAP, CD63 AG02, CD63 PLAP, Input_CD63, Input_AG02, or Input.
- the unassigned miRNAs are those that are present at similar levels in all tested subclasses, as well as the input; even representation across carrier subclasses would be a good feature for a broadly useful normalizer, and may be why unassigned miRNAs were preferentially selected as denominators for reversals.
- Ratios of normally distributed values such as our log-transformed miRNA abundances, are used frequently in risk analysis (Hayya et ah, Management Science 21(11): 1338-1341 (1975)). Ratios of log values have been shown to be particularly useful for examining a change in the rate of incidence of a clinical event. Relative log survival is an unbiased estimate of the relative hazard (Perneger, Contemp Clin Trials 29(5):762-766 (2008)).
- the ratios of logs provide a useful metric.
- the first principal components of the verified reversals in the early, middle and late blood draw windows show a strong separation between cases and controls while capturing the majority of variance.
- miRNAs expressed by non-target tissues can serve as internal normalizers that enable more robust measurement of target tissue-associated exRNA biomarkers, or that normalization of placental to maternal contributions improves predictions for fetal/maternal dyad disease states like PE.
- the verified predictors contained several miRNAs that were encoded in the C19MC and chrl4q32 miRNA clusters.
- miRNA expression data from a variety of cell and tissue types was used to determine the likely sources of the extracellular miRNA biomarkers. For the reversals, Liver, RBC, Placenta, and Platelets were the most frequent major contributors of the numerator miRNAs, and Lymphocytes were the major contributor for the large majority of denominator miRNAs.
- miRNA expression data from samples enriched from pooled pregnant serum samples by immunoaffmity separation using magnetic beads conjugated to antibodies raised against CD63, AG02, and PLAP was also used to determine the carrier subclass association of our extracellular miRNA biomarkers. Nearly half of the miRNA biomarkers for which Placenta was a major contributed were associated with PLAP, and a third were associated with CD63, suggesting that placental EVs and canonical EVs are important carriers of placentally-derived extracellular miRNAs. It is important to note that the approach for estimating the contribution of each cell/tissue type to the level of specific miRNAs in the serum assumes that the intracellular level of each miRNA is reflected in the population of miRNAs released by that cell/tissue into the serum.
- hsa-miR-650 (Ura et al., Taiwan J Obstet Gynecol 53(2):232-234 (2014)); hsa-miR-29a (Li et al., Biomed Res Int 2013:970265 (2013); Yang et al., Clin Chim Acta 412(23-24):2167-2173 (2011); Yang et al., Mol Med Rep, 12(l):527-534 (2015)); hsa-miR- 210 (Gan et al., Medicine (Baltimore) 96(28):e7515 (2017); Ura et al., Taiwan J Obstet Gynecol 53(2):232-234 (2014); Xu et al., Hypertension 63(6): 1276-1284 (2014)); hsa-miR-518b
- hsa-miR-155-5p can be a biomarker for prediction and diagnosis of preeclampsia, and can also be a functional mediator of preeclampsia pathogenesis.
- the extracellular miRNA biomarkers for preeclampsia can be indicators of placental or maternal tissue stress and/or serve as signaling molecules between the placenta and maternal tissues, or between maternal tissues.
- Three novel extracellular miRNA biomarkers identified in this study have been previously associated with hypertension. hsa-miR-26b-5p and hsa-miR-7- 5p were found to be upregulated in the plasma of non-pregnant patients with hypertension and left ventricular hypertrophy (LVH) compared to normotensive patients or patients with hypertension but no LVH (Kaneto et ah, Braz J Med Biol Res 50(12):e6211 (2017)).
- LVH left ventricular hypertrophy
- hsa-miR- 181a-5p mimic has been shown to decrease blood pressure in hypertensive mice (Marques et ah, Adv Exp Med Biol 888:215-235 (2015)).
- hsa-miR-26b-5p appears to be predominantly derived from the liver and placenta, hsa-miR-7-5p from the brain, and hsa-miR- 181a-5p from the placenta.
- the candidate predictors from this study can be validated on a large independent cohort, as individual biomarkers or as components of multianalyte assays, which can include not only combinations of extracellular miRNA predictors, but also clinical parameters, such as history of severe preeclampsia, kidney disease, chronic hypertension or abnormal analytes during first or second trimester screening.
- Validated clinical assays for predicting the risk of clinically relevant preeclampsia allows targeting of clinical resources to high-risk cases, while sparing low risk patients unnecessary anxiety they will also enable identification of high-risk cases for clinical studies aimed at personalized administration of aspirin, as well as novel preventative and therapeutic modalities.
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