WO2025250381A1 - Methods for assessment and treatment of pregnant women at risk of disorders of the placental bed - Google Patents

Abstract

This disclosure relates to methods for using methylation expression to identify individuals suspected of experiencing a pregnancy disorder and that can benefit from therapeutic intervention.

Description

Methods for assessment and treatment of pregnant women at risk of disorders of the placental bed

[0001] Related Applications

[0002] This application claims priority to U.S. Ser. No. 63/789,530 filed on April 16, 2025; U.S. Ser. No. 63/755,627 filed on February 7, 2025; U.S. Ser. No. 63/658,574 filed on June 11, 2024; and U.S. Ser. No. 63/653,407 filed on May 30, 2024. The entire content of each such application is incorporated herein in its entirety.

[0003] Field of the Disclosure

[0004] This disclosure relates to methods for using methylation expression to identify individuals suspected of experiencing a pregnancy disorder and that can benefit from therapeutic intervention.

[0005] Background Information

[0006] In their original work, Winger and Reed identified a group of maternal cell microRNAs (miRNAs) that are differentially expressed in early pregnancy between pregnant women destined to experience healthy pregnancies from those destined to experience pregnancies compromised by disorders of the placental bed (Winger EE, ReedJL et al. Am J Reprod Immunol. 2014 Nov;72(5):515- 26.; Winger EE, Reed JL et al. J Reprod Immunol. 2015 Aug; 110:22-35). A strategy was developed based on differential expression of patient maternal cellular microRNAs and corresponding microRNAs from a control group. Maturation of the placental bed begins at implantation. Blood flow to the placenta is blocked by trophoblast plugging before 10 to 12 weeks prior to which physiologic transformation of the spiral arteries is already well under way. Interrogation of plasma comprised placenta-shed components commonly performed by many in the field, thus, provide cannot provide insight into early spiral artery transformation.

[0007] Romero, Pijnenborg and Brosens devised the term “Disorders of the placental Bed” to provide a pathogenetic basis for the “Great Obstetrical Syndromes", the major syndromes of the second half of pregnancy (Pijnenborg, et al. Placental Bed Disorders, Basic Science and its Translation to Obstetrics, Cambridge University Press, 2010, ISBN 978-0-521-51785-0). These syndromes share, in common, failure of complete transformation of the spiral arteries into high-flow, low-resistance vessels, leading to placental malperfusion once blood-flow to the intervillous space is established. The syncytiotrophoblast responds to malperfusion by shedding a variety of constituents that investigators have quantified at early time points in the hope of predicting pregnancy outcome. However, all placentas, whether from patients destined to normal or compromised birth are physiologically hypoxic prior to the initiation of maternal blood flow at the end of the first trimester. Thus, pathology leading to a pregnancy disorder (e.g., placental bed disorders) cannot be detected in by examination of placentashed constituents prior to the initiation of placental blood flow. Following the initiation of blood flow to the placenta, the interrogation of plasma microRNA for placental dysfunction is further limited by the relatively minor contribution of the placental component in the admixture relative to maternal component. The placental contribution is about 3.70 percent (Tsui, et al. Clinical chemistry, 60(7), pp.954-962). By limiting microRNA quantification to maternal immune cells regulating placental bed formation, functional assessment of pregnancy risk can occur at an earlier gestational age, when preventative therapy is most effective.

[0008] Winger and Reed, initially identified microRNAs corresponding to canonic microRNA sequences found in mirBase (mirBase.org ver. 22.1 last visited 5/3/2024). However, while this allowed detection of pregnancy risk at an earlier timepoint in pregnancy than published tests (<9 weeks), it did not allow assessment of the “instant state” of the pregnancy, for example, one could not assess the immediate response to an interventional treatment unconflated by genomic and unchanging markers.

[0009] Epitranscriptomic modification occurs at both their DNA and RNA levels. While DNA modifications are limited to pre-transcriptional events, RNA expression relies upon epitranscriptomic DNA modifications directly involving DNA bases as well as epitranscriptomic modification of histones. RNA modification is limited to direct modifications of ribonucleotide bases. At least 170 different modifications have been described. While modification to DNA bases and histones is more closely related to multigenerational effects, RNA modifications are limited to post or co-transcriptional events. This suggests that RNA modifications are directly influenced by instant biologic state {Helm, etal. 2017. Detecting RNA modifications in the epitranscriptome: predict andvalidate. Nature Reviews Genetics, 18(5), pp.275-291). The addition of detection of microRNA epitranscriptomic modification to the microRNA pregnancy panel could allow such real time assessment to become possible.

[0010] Methylation of bases within the mature microRNA is inhibitory of the action of unmethylated microRNA. Differences in the fraction of methylated to non-methylated microRNA between patients suffering from a condition and those that are healthy may be an indication of pathology. The relative expression levels of methylated mature microRNA sequences have also been described in pancreatic cancer (Konno, M., Koseki, J., Asai, A. et al. Distinct methylation levels of mature microRNAs in gastrointestinal cancers. Nat Commun 10, 3888 (2019). Konno found changes in the level of m6A methylation differ from levels of canonic microRNA expression in diseased tissue. While methylation rates were higher in cases of cancer, no corresponding differences were detected by PCR. This indicated that the canonic sequences were measuring different biological effects than the differentially methylated microRNAs. Thus, m6A methylation offers new information about the current biological state of the patient.

[0011] Detection of epitranscriptomic differences, permits identification of microRNAs (which includes various small non-coding “sncRNAs”) by their methylation state as well as by the canonic expression. This could allow real-time assessment of pregnancy state. While canonic microRNA expression is regulated by both the heritable and general biologic state of the patient, the instant disclosure is directed to biomarkers better suited to detect the current “instant” biologic risk state of the pregnancy.

[0012] Summary of the Disclosure

[0013] This disclosure relates to methods for identifying in and optionally treating a disorder of pregnancy of a human being by: (a) processing human (i.e., patient) maternal immune cell sncRNA, (b) quantifying the patient unmethylated and patient methylated maternal immune cell sncRNA, and, optionally, (c) providing therapeutic intervention to the pregnant human being where the ratio of patient methylated to unmethylated patient maternal immune cell sncRNA diverges from corresponding control ratio and/or absolute or relative quantity of methylated patient maternal immune cell sncRNA. In some preferred embodiments, this disclosure provides methods for identifying and optionally treating a disorder of pregnancy of a human being during the period of 4-10 weeks of pregnancy, by (a) quantifying the amount of each of the methylated and unmethylated sncRNA in the patient sncRNA, and, optionally, (b) providing therapeutic intervention to a pregnant human being where the ratio of methylated to unmethylated and/or the absolute or relative quantity of methylated sncRNA in the patient sncRNA diverges from that of control sncRNA. Other aspects and embodiments of this disclosure are shown herein, as would be understood by those of ordinary skill in the art.

[0014] Brief Description of the Figures [0015] Figure 1. Two cyclic methylation/demethylation systems, one cycling m6A and cycling m5C, that permit rapid and dynamic changes in methylation status, (see, e.g., Su N, Yu X, DuanM, Shi N Recent advances in methylation modifications of microRNA. Genes Dis. 2023 Dec 23;12(l):101201).

[0016] Figure 2. Focal Adhesion Pathway Analysis. Related to differences in microRNA regulation at the gene-pathway level, this Focal Adhesion Pathway analysis diagram illustrates the top statistically significant KEGG pathways associated with the twenty most differentially expressed microRNAs between Blacks and non-Blacks in healthy early pregnancy using peripheral blood derived buffy coat specimens. The top 20 race-associated microRNA members are listed in boxes in the diagram. It is noteworthy that the Focal Adhesion pathway is known be associated with cell adhesion and trophoblast migration in early pregnancy. It is also noted that the pathway diagram was created by submitting the top 20 most differentially expressed microRNA in healthy early pregnancy between African-Americans (or Blacks) and Non-African-Americans (or Non-Blacks) to analysis using DIANA miRPath v.2.0: Web server issue.

[0017] Figure 3: Relationship between m6A methylation “eraser” FTO activity and pregnancy outcome, argument diagram with references.

[0018] Figure 4. FTO genotypes and minor allele frequencies by race group and cohort Frequency of rs 1421085 variants in the FTO gene differs dramatically between Black and non-Black populations. Figure taken from reference: Hallman, D. Michael, et al. The association of variants in the FTO gene with longitudinal body mass index profiles in non-Hispanic white children and adolescents. "International journal of obesity 36.1 (2012): 61-68.

[0019] Figure 5A. An unlabeled “capture” probe (probe 1) complementary to the mature microRNA is attached to the solid phase. The target RNA (mature microRNA) is end-labeled with a spectrally distinct fluorophore (probe 3). An antibody such as anti-m6A: (cat. #202 003 Synaptic Systems, Goettingen, Germany, for example) is end-labelled with a spectrally distinct fluorophore (probe 2). Probe 2 comprising the antibody is incubated with the RNA captured by the unlabeled probe 1. Fluorescent signals from binding of probe 2 represent the fluorescence due to the presence of the methylation, e.g. m6A. The fluorescence signal from the captured RNA (probe 3) represents the fluorescence of both the methylated and unmethylated fractions of the target RNA. The ratio of signal 2 to signal 3 represents the fraction of the microRNA that is methylated. [0020] Figure 5B. Pre-microRNA is shown as a “hairpin” (containing a loop structure) with the two constituents of the mature sequence (Probe 1) hybridized together. The hybridized strands together constitute the mature sequences joined together by the loop sequence constituting the pre- microRNA. Probe 1 (green) is complementary to one strand of the hybridized pair and is of sufficient length to hybridize under the selected conditions of hybridization (namely below the melting temperature of the two strands). The second probe (probe 2 -blue) is complementary to a portion of the mature sequence but of insufficient length to hybridize with the structure under the selected hybridizing conditions. The second probe is also complementary to a contiguous length of the loop structure such that it is also of insufficient length to bind under the selected hybridizing conditions but together with the portion of the probe complementary to the mature sequence will hybridize. Probes 1 and 2 are labelled with spectrally distinct fluorophores. After hybridizing, the reaction wells are washed free of unbound probe and fluorescence quantified. Precise quantification of the mature sequence is the difference between the signal derived from probe 1 less the corresponding signal from probe 2.

[0021] Detailed Description

[0022] The methods of this disclosure combine canonic microRNA quantification with epitranscriptomic modification, to allow molecular assessment of the “instant” pregnancy state. This enables functional assessment of pregnancy risk at an earlier timepoint, when preventative therapy can be effective, enabling healthier pregnancy outcomes.

[0023] While the term “methylation” is discussed herein, any epitranscriptomic modification is understood to have effects similar to those effected by methylations. While methylation at the 6^th position of adenine nucleotide is principally discussed, other adenine methylations are also included such as other adenine methylations at positions 1 and 2 as well as at 2’0 position on ribose. Methylations are also included on the remaining nucleotides. Therefore, it is understood that when the term “methylations” are discussed, other modifications are understood to be referenced.

[0024] DNA and RNA methylation and demethylation function through separate enzyme systems. RNA modification is under control of separate enzymes such as” writers” and “erasers” (METTL3, METTL14, METTL16, WTAP, ZC3H13, VIRMA and RBM15 and erasers (FTO or ALKBH5), for example, the methyl modification at N6 of adenosine (m6A) are responsive to the current biologic state of the subject. DNA modifications can be passed down through cell divisions to daughter cells and through generations constituting meiotically heritable modifications (Trerotola, et al. 2015. Epigenetic inheritance and the missing her liability. Human genomics, 9, pp.1-12). Kovalchuk suggested a contemporary definition of the epigenetics of DNA as mechanisms of heredity separate from those involved in the modification of the DNA sequence itself (Kovalchuk, et al. 2012. Epigenetics in health and disease. FT Press). Thus, these modifications, while not involving changes in DNA sequence may be heritable over generations and thereby may not directly reflect the current biologic status of the subject. Rather a combination of inherited characteristics and current events result in epitranscriptomic changes in DNA. In contrast, epitranscriptomic changes in RNA are restricted to the instant state of the organism and, in particular, to the tissues and organs in which they are resident.

[0025] The m6A “reader” proteins (IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF2, YTHDF3 and YTHDC2 and hnRNPA2Bl) recognize m6A and determine the fates of m6A-modified RNAs (Liu, et al. 2021. Potential roles of N6-methyladenosine (m6A) in immune cells. Journal of Translational Medicine, 19(1), p.25). Most frequent amongst transformed epitranscriptomic modifications is the m6A. The m6A modification is catalyzed by the “writer” proteins including METTL3, METTL14, METTL16, WTAP, ZC3H13, VIRMA and RBM15 that methylate adenine at N6, while the “eraser” enzymes, FTO and ALKBH5, remove the m6A modification. Separate systems of writers, readers and erasers are specific for m5C and m7G.

[0026] The methods disclosed herein comprise the quantification of maternal blood cell microRNA wherein modified microRNA and unmodified microRNA are quantified separately. Modification comprises any concurrent or post transcriptional addition to individual nucleotides that include the addition of methyl, ethyl, acetyl, or other covalently attached groups. Preferentially, methyl groups are attached to specific nucleotides such as adenine, cytosine or guanine. More preferentially methyl groups attached to individual nucleotide species such as adenine may be attached at specific nitrogen such as conventionally numbered as 1,2 and 6 and to the ribose at the 2’ carbon. Additionally, co-transcriptional or post-transcriptional nucleotide modifications include adenine to Inosine, cytosine to uridine and pseudo-uridylation are included. Variations from the canonic sequence may also be considered when comparing to corresponding sequences in healthy controls. Isomeric variations (variation from the canonic sequence) and incorporation of nucleotide bases with modifications varying from corresponding microRNAs in control individuals. Variance in the quantification of any of these, unmodified and modified from healthy controls is the basis for prediction of compromised pregnancy outcome indicating the need for intervention.

[0027] In a preferred embodiment, this disclosure provides methods for treatment for a disorder of pregnancy in a human being, the method comprising: (a) collecting patient maternal immune cell microRNA and optionally determining levels of modified bases by physically separating unmethylated patient maternal immune cell microRNA and methylated patient maternal immune cell patient microRNA, if present, (b) quantifying the individual patient unmethylated and patient methylated maternal immune cell sncRNAs, and, (c) providing therapeutic intervention to the pregnant human being where comparing the absolute or relative quantities of methylated and unmethylated microRNA are divergent.

[0028] In some preferred embodiments, the ratio of methylated to unmethylated sncRNAs and/or absolute and/or relative quantity of the patient microRNA diverges from the control microRNA or the control by at least about any of 1%, 5%, 10%, 25%, 50%, 100% or more and/or the patient ratio diverges by a standard deviation of about 1, 1.5, 2.0 from the mean of the control group.

[0029] In some preferred embodiments, the control microRNA is derived from maternal immune cells of a pregnant human being without a pregnancy disorder (e.g., of the placental bed) during the first or second trimester of pregnancy.

[0030] In some preferred embodiments, the biological sample comprises mononuclear cells.

[0031] In some preferred embodiments, the biological sample is peripheral blood.

[0032] In some preferred embodiments, the methods further comprise the step of extracting microRNA-comprising RNA from the biological sample.

[0033] In some preferred embodiments, the methods comprise determining a ratio by calculating expression of said at least one sncRNA (e.g., in some preferred embodiments miRNA), wherein said ratio comprises a numerator equal to the difference between the mean value of expression of the at least one sncRNA (e.g., in some preferred embodiments miRNA) in the first population and the mean value of the second population and the denominator comprises the average of the two standard deviations of the values for the first and second populations; wherein the at least one sncRNA (e.g., in some preferred embodiments miRNA) is selected from the group consisting of the sncRNAs (e.g., in some preferred embodiments miRNAs) presented herein as being capable of distinguishing the first and second populations. In some preferred embodiments, the first population are compromised pregnancy outcome individuals and the second population are healthy pregnancy outcome individuals. In some preferred embodiments, said sncRNA (e.g., in some preferred embodiments miRNA) exhibits a signal consistency of at least about 85%; a mean signal strength of at least 5.0; and a p value of less than 0.05 (p<0.05). In some preferred embodiments, the at least one sncRNA (e g., in some preferred embodiments miRNA) exhibits a of greater than or equal to about any of 1.0. 1.1, 1.2, 1.3, 1.4, or 1.5; or greater than or equal to about 1.3.

[0034] Other aspects, including useful variations thereof, are also disclosed and/or contemplated herein as will be understood by those of skill in the art.

[0035] Functions of modifications in maturating and mature microRNA

[0036] Methylations of microRNAs may be identified at different stages during maturation of the initial microRNA sequence as transcribed. The immature microRNA is transcribed as a large RNA sequence sometimes as a polycistronic assemblage of concurrently transcribed microRNAs. m6A within the primary region of an immature microRNA (pri-microRNA) can be recognized by methylation “readers” which, in turn, relay a diversity of specific actions.

[0037] Three levels of microRNA methylation affect microRNA activity. The first and second are found within methylations within the pri- and pre-microRNA segments, respectively, and third, within the mature sequence itself. The first two methylations are involved in the maturation of the initially transcribed RNA. The m6A modifications within the pri- and pre-microRNA segments, respectively, enhance recognition by specific readers driving maturation. Third, m6A modifications within the mature sequence affect registration of the mature microRNA within the binding groove of the Argonaute protein and stability of binding of the RISC complex with the target RNA. Konno observed that methylation of miR-200c-3p disrupts bonding with Ser220 of AGO changing target recognition (Konno, 2019). The m5C modification, likewise, is dynamic and reversible. It has writers, readers and erasers that are separate from those involved in m6A modification.

[0038] Alarcon recognized m6A modification within the pri-microRNA segment greatly increases the efficiency of pri-microRNA processing. The reader hnRNPA2Bl is important in microRNA maturation. Upon recognizing m6A within the pri-microRNA sequence, it recruits DGCR8 collaborating with Drosha to cleave pri-microRNA into pre-microRNA (Alarcon, et al. 2015. N 6- methyladenosine marks primary microRNAs for processing. Nature, 519(7544), pp.482-485', and Feng, et al. 2023. Effects of writers, erasers and readers within miRNA-related m6A modification in cancers. Cell Proliferation, 56(1), p.e 13340). The m6A writer/reader/eraser modification is accompanied by the m5C modification, a specific enzyme cycle involving NSUN, DNMT2 writers and TET1-3 erasers with ALYREF and YBX1 readers) and m7G modification, specific enzyme cycle involving METTL1 writer, QKI reader, where the eraser is unknown, providing additional rapidly cycling regulatory methylations (Su et al 2023. Recent advances in methylation modifications of microRNA. Genes & Diseases, p.101201).

[0039] Pandolfini found that m7G (methyl group at position 7 of guanine) within pre-microRNA disrupted guanine quadruplexes permitting formation of Watson-Crick interactions between the 3’ and 5’ arms of the pre-microRNA hairpin enabling recognition by Dicer. Thus, maturation of the pre- microRNA into the mature form was found to be enabled by m7G methylation (Pandolfini, et al. 2019. METTL1 promotes let-7 MicroRNA processing via m7G methylation. Molecular cell, 74(6), pp.1278- 1290). Methylations involving m6A, m5C and m7G are each involved in microRNA maturation. Zhang found that the YTHDF2, a reader of m6A, recognizes the m6A modification in pre-miR-126 which then binds AGO2 to facilitate final maturation (Zhang, et al. 2024. RNA m6A reader YTHDF2 facilitates precursor miR-126 maturation to promote acute myeloid leukemia progression. Genes & Diseases, 11(1), pp.382-396).

[0040] Methylation of the mature microRNA sequence directly affects target RNA binding. Cytosine methylation inhibits target RNA binding (CherayM, etal. Mol Cancer. 2020 Feb 25;19(1):36 andKonnoM. etal. Nat Commun 10, 3888 (2019)). Cheray et al. demonstrated methylation of mature microRNAs inhibits microRNA function and that a combination of DNMT3a and AGO4 paradoxically methylated cytosines (m5C) inhibiting microRNA inhibition of target RNAs.

[0041] Methylated sncRNAs can be separated from unmethylated target sncRNAs by immunoprecipitation of the methylated fraction following precipitation of the antibody complex comprising a monoclonal/polyclonal antibody directed to the methylated base and the methylated sncRNA (e.g., in some preferred embodiments miRNA) thereby physically separating the methylated from unmethylated sncRNA (e.g., in some preferred embodiments miRNA) fractions. The two fractions can be quantified separately for example, following individual labeling of the individual fractions. While methods using physical separation of the methylated and unmethylated fractions are described herein, other methods enabling discreet quantification of the individual fractions are appreciated and are covered within the disclosure.

[0042] Direct interrogation of the RNA strand by sequencing strategies that recognize modified nucleotide bases enable identification of base-specific methylation modification. As new techniques become available modification such as methylation of individual bases within the sncRNA (e.g., in some preferred embodiments miRNA) can be identified and quantified. Such refinement may improve the specificity of the assay in RNA where the length of the RNA makes recognition of the specific methylated base ambiguous. m6A, the most common methylation, is generally found in the consensus sequence DRACH (D = A/G/U, R = A/G, H = A/C/U) making it more easily predicted within the short mature microRNA sequence (Wang, et al. Deep analysis of RNA N⁶-adenosine methylation (m⁶A) patterns in human cells, NAR Genomics and Bioinformatics, Volume 2, Issue I, March 2020, lqaa007, https://doi.org/10.1093 nargah lqaa007). Modifications to the canonic sequence have significant effects on the function of any of the sncRNAs (small non-coding RNA which includes microRNA, its precursors (pri- and pre-microRNAs), tRNA and its fragments tsRNA (tRFs and tiRNA), snoRNAs and other members of the growing list of sncRNAs. These post-transcriptional and cotranscriptional modifications of RNAs are regulated by enzymatic “writers” and “erasers”, ( METTL3/METTL14) and (FTO, ALKBH5) respectively, are directive of bidirectional (m6A) methyl modification. While cotranscriptional modifications aid in processing as well as function (Gilbert, 2023).

[0043] Non-microRNA argonaut guide sequences expand argonaute repertoire

[0044] A broader range of sncRNAs are now recognized as serving as sources of guide sequences bound by Argonaute family proteins. These sncRNAs are incorporated within a “pocket” of the Argonaute protein family acting as guide sequences aligning the Argonaute protein with the target RNA. Mature microRNAs and cleavage fragments of tRNA (tRFs) are also non-coding RNAs that may be incorporated into the Argonaute binding groove (Venkatesh, et al. 2016. tRFs: miRNAs in disguise. Gene, 579(2), pp.133-138). Many tRFs are evolutionarily conserved facilitating loading into Argonaute proteins where they also appear to function as microRNAs (Kumar, et al. 2014. Metaanalysis of tRNA derived RNA fragments reveals that they are evolutionarily conserved and associate with AGO proteins to recognize specific RNA targets. BMC biology, 12, pp.1-14). [0045] MicroRNA are part of a system that integrates the incorporation of microRNAs into the Argonaute nuclease grouped together as the “RISC complex” (RNA-Induced Silencing Complex). A wide literature has been accumulated describing the incorporation of microRNA as the short noncoding RNA component of RISC. MicroRNAs are embraced within the confines of the Argonaute binding groove forming a complex where the microRNA acts to guide the complex to a target RNA with a region making a Watson-Crick complementary interaction with the microRNA. It is well appreciated that a variety of sncRNA may be accommodated within the Argonaute binding groove (Venkatesh, 2016) These include tRFs (fragments of tRNAs), endogenous siRNAs (endosiRNAs), microRNAs (miRNAs) and PlWI-interacting RNAs (piRNAs) (Ream, et al. 2015. tRNA-derived fragments (tRFs): emerging new roles for an ancient RNA in the regulation of gene expression. Life, 5(4), pp.1638-1651).

[0046] Other short non-coding RNAs are now recognized as filling the role of microRNA. These include PIWI-RNAs and siRNAs as well as tRNA fragments (tRF). The term microRNA can be broadened to include these additional short non-coding RNAs forming a complex where both the noncoding RNA and Argonaute are protected from degradation. Exemplary of these small non-coding RNAs are tRNA fragments (tRFs) which are loaded into its binding groove whereby the tRFs perform a similar role as microRNAs (Guan, et al. 2020. Inferring targeting modes of Ar gonaute-loaded tRNA fragments. RNA biology, 17(8), pp.1070-1080). Su has demonstrated diminished gene-silencing activity upon ml A modification in the seed region of tRFs negatively impacting gene-silencing activity of tRNA fragments (Su, et al. 2022. TRMT6/61A-dependent base methylation of tRNA-derived fragments regulates gene-silencing activity and the unfolded protein response in bladder cancer. Nature Communications, 13(1), p.2165). These modifications are conducted co- or post- transcriptionally indicating transformations in vivo. Thus, these transformations expand the changes to those taking place in vivo from the quantitative changes of our prior art.

[0047] Variants of the canonic pathway are well described wherein all lead to a polyribonucleotide of a limited length suitable for Argonaute incorporation. Not only are there a variety of non-canonic post-transcriptional processes, an expanding repertoire of genomic resources for potential guide sequences is becoming recognized. tRF fragments expand the inventory from which evolution can fashion responses to immediate challenges either by incorporation into the Argonaute binding groove or by alternate and direct interaction of the fragment with a targeted RNA. Further, microRNAs and tRNA fragments are themselves comprised within a growing repertoire of sncRNAs that together are involved in multiple regulatory processes such as cell migration, cell division, DNA repair and apoptosis (Zhang, etal. 2021. Small non-coding RNAs in human cancer: Function, clinical utility, and characterization. Oncogene, 40(9), pp.1570-1577). Zhang adds small nucleolar RNAs, and PIWI- interacting RNAs.

[0048] The RNA transcript when recognized by hybridization with complementary primers and probes may obscure the multiplicity of variants of the target microRNA. The addition of non-templated bases by polymerases with low fidelity add and subtract non-canonic bases. Wherein a probe comprises sequences limited to the mature sequence, nucleotides from the pre-microRNA sequence present in continuity may also be recognized thereby the pre-microRNA sequence may be simultaneously quantified together with the mature sequence. The addition of non-templated bases generates unique mature sequences of different lengths. Sequencing has permitted recognition of such polymorphisms. However, current sequencing technology is also limited. The major sequencing technology, the Illumina™ method, recognizes the natural four bases, but do not recognize nucleotide modifications except for the m5C modification recognized with the addition of bisulfite sequencing. Additional techniques supplement sequencing to identify modifications. Modification-specific monoclonal/polyclonal antibodies, e.g. anti-m6A, may be added to segregate RNA fragments with modifications from those which are unmodified. Techniques permitting clinically useful detection of nucleotide-specific modifications remain in development. Such transformation, particularly when occurring in the seed region can change the preferred target or its affinity of the microRNA by changing the sequence and possibly the identity of the microRNA as specified in MirBase.

[0049] Sequencing remains the best method for quantifying and defining target RNA isomirs. Tomasello defines isomirs by their length at the 3’ and 5’ ends as well as their post-transcriptional modifications (T omasello L, Distefano R, Nigita G, Croce CM. The MicroRNA Family Gets Wider: The IsomiRs Classification and Role. Front Cell Dev Biol. 2021 Jun 9;9:668648.). While the precise nucleotide sequence may be identified by conventional sequencing, an adenosine methylated at N6 (m6A) may be recognized only as an adenosine thereby not recording the epigenetic alteration. To overcome this limitation, a wide variety of methods have been proposed to record these additional isomeric modifications. Several utilize monoclonal/polyclonal antibodies specific to detect the individual modifications. Sequencing of RNAs precipitated by a modification-specific antibody, e.g. methylation at N-6 of adenosine (m6A), permits identification of noncoding RNAs that comprise a methylated adenosine. Sequencing or hybridization of the modified sncRNA permits their identification as a methylated specific sncRNA. Such methods do not identify the specific adenosine that is modified within the RNA. Practical sequencing methods are in development that distinguish specific modified and unmodified bases in microRNAs (Oxford Nanopore™).

[0050] tRNAs comprise a high number of epigenetic modifications. Each carry an average of 12- 14 modifications in an average sequence length of 70 to 90 bases. Structurally complex, the modifications enable proper folding and thereby function of target molecules. The process of tRNA processing is multi-step taking place in a succession of subcellular locations. The relatively recent recognition of tRNA fragments (tRFs) as modulators adds to an ever-expanding pantheon of sncRNAs (Sobala, etal. 2011. Wiley Interdisciplinary Reviews: RNA, 2(6), pp.853-862). These tRNA fragments generally range from 16-25 nucleotides in length. Significantly tRF composition, length, modification and number are strongly influenced by their context. These include tissue type, tissue state, sex, population of origin as well as race. Correlation of these factors offers opportunities to assess a current biologic state (Magee, et al. 2020. Nucleic Acids Research, 48(17), pp.9433-9448). Incorporation into a RISC complex appears to be a major way in which tRFs impact cellular activity.

[0051] Additional structural nucleotide modification include adenosine to inosine (A to I) effected by enzymes within the ADAR family and cytosine to uridine (Apobecl) and uridine isomerization such uridine transformation into pseudouridine (psi or T). tRFs (tRNA fragments) undergo progressive modifications. These isomeric differences may be indicative of prognostic groups in addition to corresponding differential expression upon integration into Argonaute proteins. The effects of epigenetically transformed nucleotides within microRNA are expected to alter recognition and binding of the RISC complex to the target RNA.

[0052] Previous studies by Winger and Reed identified differentially expressed microRNAs within peripheral blood cells (maternal immune cells) between patients who experienced late pregnancy disorders and those that experienced healthy term births (Winger EE, Reed JL, Ji X, Gomez-Lopez N, Pacora P, Romero R. MicroRNAs isolated from peripheral blood in the first trimester predict spontaneous preterm birth. PLoS One. 2020 Aug 13;15(8):e0236805. doi: 10.137 l journal.pone.0236805: Winger EE, Reed JL, Ji X, Nicolaides K. Peripheral blood cell microRNA quantification during the first trimester predicts preeclampsia: Proof of concept. PLoS One. 2018 Jan 2;13(l):e0190654. doi: 10.1371 /journal. pone.0190654.). Blood samples were collected during the first trimester in a study directed to correlation of maternal blood cell microRNA with functional assays of immune function. Of particular interest, this correlation was found in samples drawn very early during pregnancy (7-9 weeks gestational age) at a time preceding the initiation of placental blood flow thereby preceding placental malperfusion following the initiation of placental blood flow.

[0053] Epitranscriptomic alterations can alter microRNA-mediated recognition of target RNAs either by their interaction with “readers” that recognize the epitranscriptomic nucleotide alterations activating reader function or by affecting alteration in RNA structure and/or base pairing, thereby directly affecting complementarity with targeted polynucleotides or interacting with newly complementary targets.

[0054] Available methods are based on the quantification of canonic microRNA while the instant disclosure is based on the quantification of co- and post-transcriptional modifications of the canonic sequence in maternal cells or plasma. An inherent advantage of post-transcriptional modification resides in limiting response to instant events regulating the action of writers and erasers (Grimble, el al. 2000. Current Opinion in Clinical Nutrition & Metabolic Care, 3(5), pp.399-408; Laber, et al. 2021, Science advances, 7(30), p.eabg0108.). Writers and Erasers counterbalance the m6A methylation in a highly dynamic manner. m6A as regulated by “writers” and “erasers” can alter the methylation status of RNAs whereas the expression of said microRNAs may show no change in expression levels (Konno 2019). Recognition of alterations in methylation status is a significant improvement over our prior art. mlA modification within the seed region of tRFs, acting in a corresponding manner as microRNAs, diminishes gene-silencing activity of the tRNA fragments (Su, et al. 2022. TRMT6/61A-dependent base methylation of tRNA-derived fragments regulates genesilencingactivity and the unfolded protein response in bladder cancer. Nature Communications, 13(1), p.2165). The dual presence and activity of “writers” and “erasers” ensure the rapid response of the modifications, (see Figure 1) Recognition of differences in methylation state between patient and healthy controls offers the clinician a tool that informs the clinician of the instant biologic status of a patient and provides a tool for monitoring the direct and immediate effect of an intervention.

[0055] The recognition of methylated microRNAs/sncRNA is analogous to the recognition of elevated glucose levels in the diagnosis and management diabetes. Diabetes is, essentially, an incurable state. The manifestation of diabetes is elevation in glucose levels which must be managed in real time. Thus, the methods disclosed herein are directed to management of the current biologic status of the individual. Recognition of methylation state aids in management of the manifestations of a disorder of pregnancy (e.g., of the placental bed). Like management of glucose in diabetes, management of glucose levels reduces the ultimate development of diabetic complications. The methods of this disclosure interrogate expression of methylations in sncRNAs. Unlike prior art methods, the rapid and dynamic alterations in methylation status of specific sncRNAs is analogous to the type of rapid change, especially with therapeutic intervention, seen with glucose levels in diabetic patients.

[0056] The enzyme systems comprising “writers” and “erasers” of RNA methylations may be considered analogous to the phosphorylations of protein signaling molecules. For example, signaling through ITIM and ITAM immunoreceptors, ITAM-bearing receptors engage a cascade resulting in kinase-mediated activation while ITIM-bearing receptors engage a cascade resulting in phosphatase- mediated inhibition. Billadeau and Leibon describe a system that exquisitely integrates immediate cellsurface conditions challenging the cell (Billadeau, et al. 2002. ITAMs versus ITIMs: striking a balance during cell regulation. The Journal of clinical investigation, 109(2), pp.161-168). The instant conditions at the cell surface are integrated into an immediate, balanced response. Response mediated by the single modification of a signaling molecule by the addition or deletion of a phosphate group permits rapid rebalancing.

[0057] Methylations focus sncRNA (e.g., in some preferred embodiments miRNA) expression on instant/current events. Separating canonic forms as transcribed from genomic DNA and post- transcriptional modifications is also comparable to separation of the innate and adaptive immune systems. The innate immune system represents molecular adaptions that have evolved over millions of years while the adaptive immune system is generated de novo in each individual. The adaptive immune response is fashioned at the time of need and, thereby, provide a snapshot of the immediate state of the organism. An analogy can be drawn between the innate and adaptive immune systems and methylations of sncRNA (e.g., in some preferred embodiments microRNA)s. Canonic sncRNA (e.g., in some preferred embodiments mcroiRNA)s correspond to the innate immune system, comprising a record of historical as well as current events, while post-transcriptional modifications correspond to the adaptive immune system representing immediate and concurrent immune system adaptions. Collected post transcriptional modifications constitute a record of intra-lifetime events while quantification of canonic sncRNA (e.g., in some preferred embodiments miRNA)s, in large part, represent the genetic legacy of the individual.

[0058] Assessment of post-transcriptional modifications in maternal immune cells promises to sharpen recognition of adverse events and display the differential effects on their expression resulting from intervention. Methylations affect all stages from sncRNA (e.g., in some preferred embodiments miRNA) maturation to the modifications of the mature sncRNA (e.g., in some preferred embodiments miRNA). Methylations along the path after generation of pri-microRNA involve multiple opportunities for regulation. Methylations prominent in microRNA maturation and in function of mature microRNA are generally limited to m6A, m5C and m7G and possibly pseudouridine and the A-to-I and C-to-U (cytosine to uridine) transitions. Involvement of writers, readers and erasers along the path permits fine and immediate regulation of microRNA action.

[0059] It is now also appreciated that individually identified microRNAs function in part as “swarms” analogous to retroviral sequences infecting an individual where the virus exists within the infected individual as collections of variants (quasi species). The concept of quasispecies is well established in the virology literature (Domingo, et al. 2021. Historical perspective on the discovery of the quasispecies concept. Annual Review of Virology, 8, pp.51-72). Virus replication by low fidelity polimerases generate the spectrum of mutant viruses constituting the “swarm”. Over a period of time, a process of fitness selection eliminates mutant viruses with low fitness while those with greater fitness undergo continued propagation, continued mutation and continued selection. A corresponding process generating modified microRNAs comprise a wide variety of modifications responsive to innate and temporal selection pressure within the host. The relative proportions of modified to unmodified bases may provide clinical information about the relative impact of stressors (Konno 2019). The evolution of epigenetic changes in the quasispecies is a combination of effects of the local environment and its effect on individual sequence isomers. It is appreciated that some variants will be better correlated with patient status suggesting additional discriminatory features. Sequence isomers with methylations and their methylation fraction may provide additional discrimination between subject sncRNA (e.g., in some preferred embodiments miRNA) and control sncRNA (e.g., in some preferred embodiments miRNA) directing the selection of management options.

[0060] Novel use of methylated sncRNAs for early pregnancy management [0061] This disclosure relates to the unexpected utility of fractional expression of methylated sncRNAs within blood cells for use in pregnancy assessment and management. The improvement of the disclosure is novel and unexpected. The methylation status of peripheral blood cells is not expected to be correlated with the methylation status of placental bed immune cells. Differential expression of genes within specific tissues and organs relies upon unique epigenetic modification of RNA within the tissue or organ. Peripheral blood cells are integral components of systemic inflammatory conditions such as rheumatoid arthritis and systemic lupus erythematosus. The sampled cells are direct participants and mediators of the inflammatory process and directly participate in the process. Differential expression of methylated fractions between a patient and controls within blood may still be revelatory of the immunologic status of the pregnant woman while not providing specific information about events within pregnancy-related tissues.

[0062] The disclosure of Winger and Reed (see U.S. Pat. No. 10,323,282 B2 (June 18, 2019)) utilizes peripheral blood cells, a sample source not expected to reflect the immune status of a local organ, the placental bed. It is now well appreciated that immune cells mature locally in response to the local environment. The predominant immune cell in the placental bed is the natural killer cell (NK cell). NK cells in the placental bed are non-cytotoxic and contribute to placental bed development while the natural killer cells in peripheral blood are principally cytotoxic. During the first trimester, the proportion of these non-cytotoxic NK cells represent up to 50-70% of immune cells in the placental bed while NK cells in the peripheral blood constitute only about 15% of peripheral blood immune cells (Winger, E.E. and Reed, J.L., 2013. The multiple faces of the decidual natural killer cell. American Journal of Reproductive Immunology, 70(1), pp.1-9; Zhang, et al. 2021. Role of decidual natural killer cells in human pregnancy and related pregnancy complications. Front. Immunol., 12, p.728291).

[0063] The differences in cellular composition between the decidua and peripheral blood result in unpredictable biases in the raw data. Moreover, the epigenomic features of local tissues, namely the placental bed and peripheral blood are distinct. Thus, sncRNA (e.g., in some preferred embodiments miRNA) expression levels in peripheral blood are not interchangeable with sncRNA (e.g., in some preferred embodiments miRNA) expression in the decidua. Expression levels of modified sncRNA (e.g., in some preferred embodiments miRNA) in the blood are not expected to be representative of modified sncRNA (e.g., in some preferred embodiments miRNA) expression levels in the decidua. Notwithstanding, differential expression of peripheral blood microRNA between healthy and compromised patients has previously been demonstrated by Winger and Reed. MicroRNA methylation patterns in peripheral blood are responsive to the systemic biologic status which may be amenable to systemic intervention (Cojocarn, et al., 2011. Manifestations of systemic lupus erythematosus. Maedica, 6(4), p.330). The systemic inflammatory state and local organ-specific immune responses are bidirectionally reflective of one another (Krausgruber, et al., 2020. Structural cells are key regulators of organ-specific immune responses. Nature, 583(7815), pp.296-302).

[0064] This disclosure provides novel methods of improving the prior art disclosure (Winger, Reed: U.S. Pat. No. 10,323,282 (June 18, 2019)) by identifying sncRNA (e.g., in some preferred embodiments miRNA) together with their methylation status. In addition, the method provides means for identifying the methylated sequence isomers that may be better correlated with a clinical state than the whole sequence itself It is contemplated that individual isomir methylation may be more predictive of a response to intervention than the methylation level of the canonic form.

[0065] The relative expression of methylated to unmethylated microRNA has been shown to discriminate between healthy and patients with pancreatic cancer (Konno, 2019). The reagents and methods of this disclosure provides a superior and improved tool to determine the need for clinical intervention. The rapid turnover of methylations enabled by enzymatic writers and erasers facilitates identification of the instant state of the individual while interrogation prior to and following intervention improves assessment of the success of a selected intervention (see Figure 1). The reagents and methods of this disclosure provides the clinician with a tool for identifying modifiable risks. Rather than a diagnostic test limited to assessing intrinsic and heritable factors that may not be amenable to intervention, the instant disclosure is directed to notifying the clinician of factors that indicate the usefulness of an intervention and a method for assessing the efficacy of the intervention.

[0066] While some of the methods disclosed herein provide a means of identifying changes within the first and early second trimesters, labile methylation changes can also be exploited by methods disclosed herein that inform clinicians about the instant clinical state throughout pregnancy. The effects of malperfusion affect detectable changes within sncRNAs throughout pregnancy. It is understood that maternal cell sncRNA (e.g., in some preferred embodiments miRNA) expression at an early timepoint best assesses spiral artery maturation. The process of spiral artery maturation continues into the second trimester. Deficient spiral artery transformation results in malperfusion of the placenta. The malperfused placenta responds by shedding increasing quantities of trophoblastic debris as the placenta grows (Redman, et al., 2012. Placenta, 33, pp.S48-S54). Redman indicates that much of the shed trophoblastic debris is pro-inflammatory and acts systemically. Systemic pathology includes hypertension and urinary protein amongst major sequelae. Excessive release of soluble fms-like tyrosine kinase 1 (sFLTl) and soluble endoglin (sENG) amongst substances that mediate developing systemic disease, such as preeclampsia. Shen et al. observe maternal obesity alters the level of RNA methylation in the term human placenta. They found placentas of obese women suffered fetal and placental hypoxia and responded with diminished placental m6A RNA expression (Shen, et al., 2022. Maternal obesity increases DNA methylation and decreases RNA methylation in the human placenta. Reproductive Toxicology, 107, pp.90-96). Inflammatory changes within the malperfused placenta are reflected in an altered proportion of methylated microRNA like the changes in the methylated fraction of microRNAs seen in cancer patients (Konno, 2019) While shed debris from the placenta gradually increases in quantity as the placenta grows regardless of any placental pathology, the methylated fraction of sncRNA (e.g., in some preferred embodiments miRNAjs remains independent of the total quantity of shed sncRNA (e.g., in some preferred embodiments miRNA). Changes in methylation identified by Shen in placentas at term indicates methylation remains a marker through the course of pregnancy. Changes in methylation state identified in the acellular constituents of blood may signal additional and potentially adverse events arising from the distressed placenta. These are distinct from those of those involved in the early stages of pregnancy arising from the placental bed that are better characterized in maternal immune cells. Such changes may provide guidance to the clinician wherein sncRNAs are extracted from the acellular components of blood at any time following the initiation of blood flow to the placenta. We believe that systemic nature representing systemic response may permit assessment of pre-pregnancy features allowing for prediction of the success of a future pregnancy. Were such features supported by examination of pre-pregnant blood cells, the success and preparation for pregnancy and IVF (in vitro fertilization) can be anticipated and appropriate measures taken in preparation.

[0067] The methods disclosed herein are improvements of the methods disclosed by Winger and Reed (see U.S. Pat. No. 10,323,282 B2 (June 18, 2019)) and can be combined therewith. The methods of this disclosure provide additional information/data that can be used to predict the utility of clinical intervention, including those currently being used and/or those that may be developed in the future. This disclosure provides sncRNA (e.g., in some preferred embodiments miRNA)-based tests and protocols for treating pregnant human beings at risk for a disorder during pregnancy (e.g., of the placental bed), as well as reagents and/or kits relating to the same. In some embodiments, this disclosure provides reagents and methods for identifying at least two characteristic groups in a patient population on the basis of sncRNA (e.g., in some preferred embodiments miRNA) expression in maternal immune cells from peripheral blood comprising the steps of a) quantifying at least one sncRNA from a biological sample derived from maternal immune cells, and b) further segregating individual sncRNAs by their methylation status and c) segregating the patient population into groups on the basis of expression of the at least one sncRNA (e.g., in some preferred embodiments miRNA) identified within studies provided. Herein, individual sncRNAs are treated as separate sncRNAs based upon their methylation status. The at least one group shall define candidates that might benefit from a therapeutic intervention. In some embodiments, at least one post-intervention assessment is performed bridging the at least one therapeutic intervention. The efficacy of the intervention can be assessed by the level of modulation of sncRNA (e.g., in some preferred embodiments miRNA) expression level and methylation level toward the control subject levels.

[0068] The improvement quantifies co-transcriptional and post-transcriptional modifications to sncRNA (e.g., in some preferred embodiments miRNA) improving both specificity and focus upon concurrent changes in sncRNA (e.g., in some preferred embodiments miRNA) expression. The relative fraction levels of such modifications as methylations together with their expression on sncRNA (e.g., in some preferred embodiments miRNA) isomirs are identified. The improvement directs focus to the concurrent biologic state of the subject enhancing predictive power and augmenting the assessment of interventional effectiveness upon evaluation of pre- and post-intervention testing. Assessment of the fraction of sncRNA (e.g., in some preferred embodiments miRNA) isomirs may further augment assessment.

[0069] Definitions used in the specification

[0070] As used herein, the term “about” when referring to a value or to an amount of mass, weight, volume, concentration is meant to encompass variations of some preferred embodiments of ±20%, in some preferred embodiments ±10%, in some preferred embodiments ±5%, in embodiments ±1, in some preferred embodiments ±0.5%, and in some preferred embodiments ±0 0.1% from the specified amount, as such variations are appropriate to perform the disclosed methods. Further, sequences, such as sncRNA (e.g., in some preferred embodiments miRNA) sequences and their precursors may vary but still fall within the definition thereof. These variations may comprise alleles that are expressed in the genome. Variations are also recognized that the result of post translational variations such as by deletions or additions of bases at either the 5’ or 3’ end of the polynucleotide usually by enzymatic activity.

[0071] Within this disclosure, the term “non-placental biological sample” shall mean maternal cells (preferably maternal immune cells) and derivatives thereof not collected or isolated from the placental site, for example collected or isolated from maternal peripheral blood, of a subject, for example a pregnant human being. A non-placental biological sample comprising maternal immune cells may be derived from an individual being a candidate for intervention, preferably during the first and second trimesters of a pregnant woman. A period of at least six months prior to the initiation of pregnancy and six months following a pregnancy are also included. As used herein, “pregnancy” or “pregnant” of a human being generally refers to the period of about six months prior to implantation and six months following parturition. Obstetrical management of pregnancy involves the perinatal period of about six months surrounding the period of gestation. In some preferred embodiments, pregnancy or pregnant refers to the typical nine months of pregnancy in a human being. In some preferred embodiments, the methods disclosed herein can be used during pregnancy and/or within a particular part of pregnancy (e.g., preferably the first, or the first and/or second trimester). As used herein, the term “plasma” includes the term “serum”.

[0072] As used herein, the term “subject” refers to any mammal, including both human and other mammals. A “control subject” is an individual(s) of comparable characteristics to a patient subject characterized by age, sex, race and/or condition (e.g., pregnant) who does not have or is destined to develop a placental bed disorder. The term “control sample” as used herein refers to a non-placental biological sample of a control subject, taken from the same source, such a peripheral blood, and collected under the same or comparable conditions including time of acquisition as a patient sample. In some embodiments, the term “control sample” as used herein may represent the mathematical mean of multiple samples from control individuals wherein the number of samples is that considered sufficient (e.g. representative) by an individual of ordinary skill in the art are collected. Additional statistical parameters may be derived from said samples such as the standard deviation of the mean. Said additional statistical parameters may be used for purposes of comparison of a patient test result with control samples to estimate the probability that the patient's test result represents an abnormal finding and, thereby suggesting that the patient might benefit from a therapeutic intervention. Preferably, the subjects to whom the methods described herein are applied are human beings, most preferably pregnant human beings. As used herein, the term “patient” shall mean an individual that is the subject of the testing procedure. It may be a person being compared to the control group. Certain data is presented with reference to subjects with ancestral origin (i.e., “race”) in sub-Saharan Africa who are referred to herein as “Black” or “Non-Black”. For the purposes on this disclosure, as used herein, the term “Black” shall mean Homo sapiens who remained in sub-Saharan Africa following the diaspora of a group of Homo sapiens out of Africa estimated at approximately 50,000 to 100,000 years ago. Non-Black subjects are human beings of a race not included within this definition of Black.

[0073] In preferred embodiments, this disclosure includes methods in which one or more sncRNAs and/or its modifications are quantified in peripheral blood (preferably maternal immune cells) of patients in the first or early second trimester of pregnancy to corresponding one or more control patients to identify differential expression predicting the need for medical intervention (e.g., in some preferred embodiments an immunosuppressant. As used herein, the term “divergent” shall mean an expression level that exceeds a statistically set value by one of ordinary skill in the art. For example, where a statistically adequate number of expression levels of control samples are used to calculate a mean and standard deviation, an individual of ordinary skill in the art may determine that values diverging by person of ordinary skill a number of standard deviations determined by the investigator, for example ±1.5 S.D., are determined to be divergent.

[0074] This disclosure defines “canonic microRNAs” as those sequences listed in mirBase.org (mirBase.org ver. 22.1 last visited 5/3/2024) and assigned a unique accession number. Wherein a microRNA sequence comprises a base not found in the canonic sequence as listed in mirBase.org and assigned a unique accession number, the sequence, as used herein, is considered a non-canonic sequence or an isomir. Post-transcriptional alterations are described by Tomasello that include a broad range of non-canonic microRNAs (Tomasello 2021). For the purposes of this disclosure, the list of Tomasello is expanded to include all post-transcriptional modifications such as methylations, base transformations such as adenosine to inosine transformation induced by ADAR and nucleotide transformation such as uridine isomerization (pseudouridine (psi or T)) to constitute non-canonic microRNAs. As used herein, the term “sequence isomer” will refer to isomers that vary by their nucleotide sequence from the canonic sequence. It is understood that any microRNA comprising a base not comprised in the canonic microRNA as listed in mirbase.org with an accession number is considered a unique microRNA.

[0075] Winger and Reed (see U.S. Pat. No. 10,323,282 B2 (June 18, 2019)) define the term “microRNA(s)” as used in this specification to include precursors of the mature microRNA sequence including pri-microRNA and pre-microRNA. Variations in the nucleotide sequence from the canonic sequence listed with unique accession numbers identified in mirBase are contemplated, as used herein, incorporated into the definition of “microRNA”, sncRNAs (small non-coding RNAs) equal or less than 200 bases) are also incorporated in the definition. Also included within the definition are epigenetic modifications including both modifications such as methylations and transformation of bases themselves such as those found in adenosine to inosine base exchanges (adenosine deaminase acting on RNA (ADAR)) and base modifications such as pseudouridine (psi or T). SncRNA (e.g., in some preferred embodiments miRNA) comprising at least one modification are treated as individual and separate from their canonic counterparts. Modified bases have different effects than unmodified bases and, thereby, are treated herein as novel bases. A microRNA comprising an “A” and a microRNA of the same sequence comprising an “m6A” are, within this disclosure, regarded as an individual and separate microRNA. SncRNA (e.g., in some preferred embodiments miRNAs comprising a modified base are herein considered novel sncRNA (e.g., in some preferred embodiments miRNAs. Those that are otherwise non-canonic sncRNAs (e.g., in some preferred embodiments miRNAs) (term used interchangeably with isomirs) are also considered novel. The additional sncRNAs such as tRNA fragments are known to be incorporated into the RISC complex and, thereby, may act as sncRNAs (e.g., in some preferred embodiments miRNAs). It is understood that tRNA fragments may be functionally active in modifying of cellular processes other than by their incorporation into Argonaute proteins. Thus, tRNA and its fragments tsRNA and tRFs and other members of the growing list of sncRNAs are included within the definition of sncRNAs (e.g., in some preferred embodiments miRNAs) as used herein. As used herein, the term “fraction” shall be used interchangeably with percent. As used by Konno (Konno 2019), a ratio of the methylated to unmethylated microRNA is recognized by those skilled in the art can be compared with the corresponding one or more microRNAs to demonstrate a variation from the control group.

[0076] As used herein, the term “maternal sncRNA (e.g., in some preferred embodiments miRNA)” shall mean sncRNA (e.g., in some preferred embodiments miRNA) isolated from maternal blood cells, and, in preferred embodiments, maternal immune cells. MicroRNAs (miRNA) comprise a class of non-coding RNAs of about a 22-24 bases. They integrate disparate genetic elements and pathways into collaborative metabolic and signaling pathways. SncRNA (e.g., in some preferred embodiments miRNAs) form networks that supervise coordinated expression of RNAs that guide and maintain, in turn, cell identity and buffer cell systems against changing conditions. MicroRNAs are known to be involved in embryonic development and have attracted great interest in the assessment and monitoring of various conditions including cancer, autoimmune, inflammatory and neurologic diseases (dePlanell- Saguer, et al., 2011. Analytica chimica acta, 699(2), pp.134-152). In previous studies, we determined that first trimester peripheral blood mononuclear cell (PBMC) microRNA provides sensitive and specific prediction of preeclampsia and preterm birth when sampled within a range of 4-14 weeks gestation (Winger et al. PLoS One. 2017 Jul 10;12(7):e0180124; Winger et al. PLoS One. 2018 Jan 2;13(l):e0190654). As used herein, specific microRNAs may be identified by their prefix “mir”- and a corresponding numeric identifier, such as mir- 155. Sequences within an RNA transcript targeted by microRNAs may lie anywhere within the transcript. However, sequences within the 3' untranslated region are most common. MicroRNA nomenclature comprises a three-letter prefix “mir” followed by a number assigned generally in order of the description of the microRNA. In one convention, when the “R” is lower case, the sequence refers to the pre-microRNA while when upper case is employed (miR), the mature form is indicated. Variants where the sequences vary by one or two bases may be designated by the letters “a” and “b” . Occasionally, pre-microRNAs located within separate regions of the genome result in an identical mature microRNA. These microRNAs are distinguished by a numeric suffix (e.g., “miR-123-1” and “miR-123-2”). When two microRNAs originate from opposite arms of the same pre- microRNA hairpin they are designated with the suffix-3p or -5p according to whether the 3' or 5' strand is used. As used herein, the numeric code, e.g., “mir-123” shall include its variants such as mir-123-1, mirl23-2, and the -3p and -5p variants. However, these variants are regarded as canonic sequences as they are listed within mirBase.org. As used herein the term “pri -microRNA” shall mean the RNA targeted by the Drosha-Pasha complex. It may also be defined as the RNA sequence directly transcribed from DNA. The term “pre-microRNA” shall mean the product of the cleavage by the Drosha-Pasha complex in the canonic pathway of maturation. In early work, investigators may not have distinguished microRNA by the arm incorporated into the Argonaute protein. In those instances where parent nomenclature, for example mir-123 and any more selective sequence for example mir- 123-5p, shall be considered interchangeable. Specific microRNA abbreviations may also include an additional prefix identifying the species of origin, such as “hsa” for homo sapiens. MicroRNAs typically comprise approximately 18-25 nucleotides, in some embodiments, about 22 nucleotides. Nomenclature for microRNAs as used herein may be found in miRBase (mirbase.org), the entries of which represent the predicted hairpin portion of the microRNA transcript. MicroRNAs are also grouped into families. MicroRNAs within a family often share common evolutionary paths, regulate common pathways and are often functionally redundant. For example, a well described microRNA family known as Let-7 defines a group of microRNAs with common functions and sequences that vary by a few bases but retain similar functions varying in their tissue site and specific function. Other microRNA families with members are the mir-15/-16 family, mir-17 family, mir-19 family, mir-29 family and there are many others (HCNC database supported by National Human Genome Research Institute (NHGRI) grant U24HG003345 URL: https://www.genenames.org/data/genegroup/#l/gt"oup/476, last accessed 6/13/2023).

[0077] Although the primary embodiments described herein are directed to humans, one of skill in the art will appreciate that, in some embodiments, the methods provided in this disclosure can be applied to other mammalian species. Mirtrons are the result of an alternate pathway of synthesis of microRNAs substituting a special class of intronic miRNAs whereby pre-miRNAs are derived directly from an intron by a process involving splicing and lariat debranching by DBR1 in lieu of Drosha- DGCR8 (Pasha) cleavage. The mature microRNA sequence is encoded within genomic DNA and is transcribed as a long RNA sequence of many hundreds to thousands of nucleotides as primary microRNA (pri-microRNA). In the canonical path of microRNA maturation, the pri-microRNA is transcribed from DNA in the nucleus comprising a single-stranded non-coding RNA disposed into secondary structures comprising stem-loops. The modification by m6A is recognized by hnPRNA2B 1 which recruits DGCR8 (Pasha), a component of the microprocessor complex which incorporates the RNase Drosha, binding RNA at the base of the stem cotranscriptionally (Alarcon 2015). The binding site of a first endonuclease (RNase), Drosha, measures up 11 base pairs from the basal single-stranded to double-stranded junction aligning Drosha for precise cleavage (Nguyen, et al., 2015. Cell, 161(6), pp.1374-1387). A hairpin product of about 65-70 bases is exported from the nucleus by Exportin 5 (XPO5) where it is further processed by a second endonuclease (RNase), Dicer, cleaving away the loop sequence leaving the stem to form a double-stranded RNA of about 22 base pairs. The RNA duplex is incorporated into the binding groove of Argonaute where the strands are separated and one strand (the guide strand) retained in the Argonaute binding groove. While exportin 5 is associated with the canonic path of microRNA generation often in times of challenge, alternative methods such as the mirtron path are exported with the aid of Exportin 1 in non-stressed times.

[0078] Wu distinguishes different categories of post-transcriptional microRNA variation based on their nucleotide sequence. (Wu, et al., 2018. BMC genomics, 19, pp.1-12). Additional isomirs comprise modifications to individual nucleotides. Tomasello (Tomasello, 2021) further describes a variety of these epitranscriptomic changes to include those created by methyltransferases, deaminases, uridyltransferases, poly (A) RNA polymerases and exonucleases (De Almeida, et al. (2018). Wiley Interdiscip. Rev. RNA 9:el440. doi: 10; Lan, et al. (2019), Cancer Res. 79, 1285-1292. doi: 10.1158/0008-5472.CAN-18-2965; Yu, et al. (2020). Nat. Rev. Mol. Cell Biol. 21, 542-556. doi: 10.1038/s41580- 020- 0246- 8).

[0079] PlWI-interacting RNA (piRNA) comprise a large class of RNA molecules that function in embryonic development to silence transposon transcription, and to suppress their integration into DNA and translation. They are incorporated into the PlWI-protein which is a highly conserved RNA-binding protein belonging to the same ancient Argonaute/PIWI family utilized by microRNA in formation of the RISC complex. RNA interfering RNAs (siRNA) form a group of non-coding pairs with complementarity to a target RNA.

[0080] Non-canonic variants (isomirs) of microRNA constitute a major fraction of transcribed microRNAs and can be as prevalent as the canonical microRNAs as listed in mirBase (mirBase.org) (Karlsen, et al., 2019. Scientific reports, 9(1), p.19999). MicroRNA may be generated non- canonically from introns to form Mirtrons- Other non-canonic pathways use only one of the two major nucleases involved in the canonic pathway, Drosha and Dicer using only one in each of two alternate non-canonic pathways (Langenberger, et al., 2013. J. Exp. Zoology Part B: Molecular and Developmental Evolution, 320(1), pp.35-46).

[0081] Epitranscriptomics constitutes an additional layer of microRNA regulation that comprise post- or co-transcriptional modifications to the entirety of microRNA including changes to both the primary (pri-) microRNA sequence and the pre-microRNA sequence facilitating engagement of nucleolytic enzymes. Modifications include methylations to the bases and post-transcriptional replacement of nucleotide bases such as by enzymatic processes, for example, wherein adenosine is deaminated resulting in transformation into inosine. Other nucleotide base transformations incorporate isomerization of uridine to form pseudouridine (psi ^P) and other variants of uridine. Such transformations may change the RNA target of the microRNA. Pri-microRNA sequences are quite long ranging from hundreds to thousands of bases (Denli,et al., (2004), Nature, 432(7014), pp.231-235). The most common epitranscriptomic modifications include adenosine methylated at N6, pseudourindine (T), Adenosine-to-Inosine (A-to-I) editing and 5-methyl-Cytidine (m5C), (De Paolis, et al., 2021. Cancers, 13(13), p.3372). The effects of these modifications are multiple. They include enhancement and inhibition of target RNA binding, modification of microRNA stability, degradation, as well as change in the target RNA amongst other effects. Alterations in nucleotides between the 5’ and 3’ can affect target RNA affinity as well as alteration in the RNA target sequence especially if the altered base is in the seed region. Deaminases such as the ADAR family and the APOBEC family convert adenosine to inosine and cytosine to uridine, respectively (Bhakta, etal., 2022. Genes, 13(9), p.1636).

[0082] Pseudouridine (psi T) is created by isomerization of uridine resulting in increased stability. PUS7, PUS1, RPUSD2, TRUB1 and TRUB2 are exemplary of enzymes in humans resulting in isomerization of uridine to pseudouridine. Importantly, m6A modifications are reversible and can be reversed by demethylase in a regulated manner (FTO, ALKBH5) (Meyer and Jaffrey 2014; Liu and Pan 2016; Zhao et al. 2017). Such reversible changes result from in vivo events such as stress and may, therefore permit assessment of reversible states amenable to intervention. Unlike m6A, pseudouridine formation appears not to be reversible (Meyer KD, Jaffrey SR. 2014. Pseudouridine may be read as cytidine thereby changing affinity as well as target RNA (Karijolich, et al., 2011, Nature, 474(7351), pp.395-398). Wang extends reversible modifications broadly to methylations (Wang, et al., 2014. Molecular Cell, 56(1), pp.5-12). The methylation/demethylation stoichiometry may be set on a cell-specific basis. Transition between methylated and demethylated status may be rapid enabling rapid response to external conditions. The most common epitranscriptomic modifications of sncRNAs are m6A, A-to-I editing, 2’-0 methyl, m7G, pseudouridine and m5C and hydroxy-m5C (hm5C) (De Paolis 2021). The reversibility of methylations, specifically at sites of m6A, m5C and m7G are significant in the instant disclosure. The most useful methylations, however, may be those that are rapidly reversible. Demethylation of these modifications operates through an oxidative path wherein, for example, m5C is first oxidized to hydroxymethylcytosine (hm5C), Formylmethylcytosine (fm5C) and then to carboxylmethylcytosine (cam5C) and ultimately transformed into cytosine. While the catabolic path is generally brief, hm5C may be stably detectable in such organs, for example, as brain. [0083] Currently available methods rely upon identification of differentially expressed canonic microRNAs for the prediction of disorders of pregnancy (e.g., of the placental bed). The methods of this disclosure identify modifications that are reversible wherein sncRNAs are integrated into a circuit comprising both “writers” and “erasers” of the specific modification. Their deployment enables facile transformation of microRNAs to and from a methylated state. Balance in such circuits is affected by local conditions beyond those directly linked to an unchanging genome.

[0084] The methods of this disclosure improve existing methods, and thereby solve existing art- recognized diagnostic and therapeutic problems, by selectively informing the investigator about ongoing and immediate changes untethered by inherited features within the genome. It is anticipated that changes in the fraction of modified microRNA may be a parameter enabling detection of and monitoring of adverse conditions. It is further expected that monitoring of such changes from controls may be useful throughout the entirety of pregnancy. Further, adverse conditions may be identified in the pre-conception period enabling clinicians to identify patients that might benefit from intervention and as well as following pregnancy. Moreover, interventions may be offered throughout the preconception period and through pregnancy (Winger, et al. J. Reprod Immunol. 2015 Aug; 110:22- 35).

[0085] The methods of this disclosure expand examination limited to canonic sequences to non- canonic sequences, in particular, to those possessing labile markers such as m6A (Shi., 2019. Molecular Cell, 74(4), pp.640-650; Zhou, etal., 2015. Nature, 526(7574), pp.591-594; Chan, et al., 2010. PLoS genetics, 6(12),p.el001247; Dedon, etal., 2014. Chem. Res. Toxicology, 27(3), pp.330-337; Schaefer, M.R, 2021. Genes, 12(3), p. 345). The conventional means of comparing one or more canonic microRNAs between the subject sample and the control sample can be limited to comparison of individual non-canonic microRNAs or combinations thereof. Further it can incorporate ratios of methylated to unmethylated sequences wherein the numerator may be the methylated sncRNA (e.g., in some preferred embodiments miRNA) and the denominator is the unmethylated sncRNA (e.g., in some preferred embodiments miRNA). The sncRNA (e.g., in some preferred embodiments miRNA) in the numerator and denominator comprises identical sequences varying only by the modification.

[0086] Methods

[0087] Suitable techniques for isolating cells from a non-placental biological sample (preferably maternal immune cells such as immune cells) that can be used in the methods of this disclosure can include isopycnic density-gradient centrifugation or monoclonal antibody superparamagnetic bead conjugates, for example, as are well-known known in the art, as well as any other suitable techniques that are available to those of ordinary skill in the art. In some embodiments, this disclosure provides methods comprising providing a non-placental biological sample (preferably maternal blood cells). Such a non-placental biological sample can be derived from cells of the biologic sample such as, for example, peripheral blood (e.g., whole blood), the buffy coat thereof (i.e., the fraction of an anti coagulated peripheral blood sample that contains most of the white blood cells and platelets following centrifugation and fractionation as by pipetting of the blood), bone marrow. Maternal mononuclear cells may also be isolated as taught by Boyum (Scand J Immunol 17: 429-436 (1983) As used herein, platelets are considered to be blood cells. In a preferred embodiment, for example, a sample derived from a peripheral blood and/or bone marrow can be limited to include a leukocyte population(s), for example, monocytes, lymphocytes, granulocyte, platelets, and/or stem cells being segregated by means well known in the art permits selective quantification of sncRNAs within that cell population. Further, for example, cell subpopulations (e.g., T cells, B cells) can be individually interrogated following their selective isolation by techniques such as, for example, flow cytometric sorting following interaction with fluorescently-labeled monoclonal antibody combinations that are capable of characterizing the individual subclasses. Blood cells may also be isolated by binding them to paramagnetic particles functionalized with monoclonal antibodies or other factor reactive with a marker useful for isolating the desired cell type.

[0088] It is understood by those of ordinary skill in the art that the sncRNA (e.g., in some preferred embodiments miRNA) content of a sample enriched for peripheral blood cells (e.g., the buffy coat) or even whole blood is representative of the sncRNA (e.g., in some preferred embodiments miRNA) content of blood cells in that sample because the sncRNA (e.g., in some preferred embodiments miRNA) content of peripheral blood cells is vastly greater than that of plasma which comprises sncRNAs of fetal origin. Thus, in preferred embodiments, a buffy coat specimen or even a whole blood specimen is essentially equivalent to a mononuclear cell specimen so long as the specific sncRNA (e.g., in some preferred embodiments miRNA) quantified is in sufficient excess of the sncRNA (e.g., in some preferred embodiments miRNA) in the non-cellular components of the sample that said quantification provides clinically equivalent results as those derived from purified maternal immune cells such as peripheral blood mononuclear cells (PBMCs). Various methods for detection and quantification of sncRNA (e.g., in some preferred embodiments miRNA), for example by hybridization (e.g. polymerase chain reaction (PCR)) and sequencing such as “next generation sequencing” may be used.

[0089] Three compartments comprising sncRNAs may thus be considered. The first is the compartment comprising maternal immune cell sncRNA (e.g., in some preferred embodiments miRNA). The second compartment is sncRNA (e.g., in some preferred embodiments miRNA) comprised in the acellular portion of whole blood, e.g. plasma or serum which comprises both maternal and fetal sncRNAs. The third compartment comprises fetal sncRNA (e.g., in some preferred embodiments miRNA) wherein interrogation is confined to those sncRNAs that are recognized as of placental origin.

[0090] Methods of Detection and Analysis

[0091] Commercial reagents and kits may be configured to recover short RNA polynucleotides of sncRNA (e.g., in some preferred embodiments miRNA) length, for example “High Pure miRNA Isolation Kit” (product number 05080576001) Millipore Sigma. Commercial reagents with accompanying instructions are widely available. It is understood herein that detection of sncRNA (e.g., in some preferred embodiments miRNA) may include detection of the presence or absence of a specific sncRNA (e.g., in some preferred embodiments miRNA) within a non-placental biological sample, and more preferably its quantification. The methods may produce dichotomous (positive or negative), semi- quantitative or quantitative results. It is understood that relative quantification wherein comparative levels between the sample of the patient is related to the level in a control or other sample particularly wherein sequential samples are assayed. Any detection method well known to those skilled in the art falls within the scope of the instant disclosure. While detection of microRNA by hybridization is limited to identifying microRNA identified as belonging to a particular MirBase catalogue number, it remains useful for microRNA quantification. In situ hybridization with methylation-specific monoclonal/polyclonal antibodies is thus limited. However, such polyclonal antibodies may also be used to precipitate modification-bearing sncRNAs segregating modification-bearing from nonmodification-bearing sncRNAs permitting sequencing specific to each of the segregated RNAs (Arraystar.com: “Small RNA Modification Service ” last accessed 5/4/24). It is, therefore, appreciated that the signal derived from monoclonal/polyclonal antibody binding or segregated methylated sncRNA by immunoprecipitation by said monoclonal/polyclonal antibody and captured by interaction with a complementary sncRNA sequence bound to the stationary solid phase complimentary to the sncRNA (e.g., in some preferred embodiments miRNA) may be ratioed to the signal generated by the quantity sncRNA (e.g., in some preferred embodiments miRNA).

[0092] In some embodiments, RNA can be extracted from cells of the non-placental biological sample (preferably maternal immune cells) according to well-known technique. Blood collected can be drawn into tubes comprising an anticoagulant preferably heparin or EDTA and maintained at room temperature preferably for as long as 24 hours prior to isolation of cells. Buffy coat, prepared according to standard procedures by centrifugation. RNA sampling and extraction: cells or sorted cell populations ( P I O⁷ viable cells) are collected in 1 ml Trizol (Invitrogen) and stored at -80°C until use. Total RNA can be isolated according to standard techniques, such as using the Trizol reagent/protocol (Invitrogen) or RNeasy Mini Kit (Qiagen). Extraction technique must capture short RNA below 20 bases. Total RNA yield can be assessed using the Thermo Scientific NanoDrop 1000 micro-volume spectrophotometer (absorbance at 260 nm and the ratio of 260/280 and 260/230), and RNA integrity assessed using, e.g., the Agilent's Bioanalyzer NANO Lab-on-Chip instrument (Agilent) used per manufacturer’s instructions. sncRNAs may be quantitated by any suitable technique including but not limited to quantitative real time PCR (qPCR using, e.g. SYBR Green, a TaqMan probe, locked nucleic acid probe (Vester, et al. Nature Methods, 7: 687-692 (2004), microRNA arrays, next generation sequencing (NGS) techniques (e.g., TruSeq kits (Illumina); (Baker et al. Biochemistry, 43: 13233- 13241 (2010)), multiplex microRNA profiling assays. When the solid phase is a microarray slide, fluorescently labeled fractions (methylated and non-methylated) can be quantified by the methods of Wang et al. (Wang et al, The m6A methylation profiles of immune cells in type 1 diabetes mellitus, Frontiers in Immunology, 15 Nov 2022, 13:1030728)

[0093] In some embodiments, the expression of various sncRNAs in a non-placental biological sample (preferably peripheral blood immune cells (maternal immune cells)) of an individual can be collected and assembled to provide a sncRNA (e.g., in some preferred embodiments miRNA) signature for that individual. Analysis and/or comparison of a sncRNA (e.g., in some preferred embodiments miRNA) signature of a non-placental biological sample may be compared with a corresponding sncRNA (e.g., in some preferred embodiments miRNA) signature derived from a control sample and/or a database representative of a control sample. Mathematical approaches to analysis of data and methods for comparison of samples to controls are well known to those skilled in the art. [0094] Sequencing methods may be utilized for quantification as well as for identification of isomers and alleles (including isomers defined by their post-transcriptional modifications such as methylations) utilizing methods well known to those with ordinary expertise in the art. Probe sequences may be configured to hybridize specifically with epigenetically altered bases such as methylated bases or non-canonic bases within a sequence such as inosine wherein the canonic base is adenosine or uridine wherein the canonic base is cytosine. Additional methods for identification of bases modified such as by methylation at adenosine nitrogen 6 employ monoclonal or rabbit polyclonal antibodies that recognize M6A. Nanopore technologies, as provided by Oxford Nanopore Technologies is currently developing methods for sequencing that are able to distinguish epigenetic modifications to individual bases from unmodified bases in sncRNAs.

[0095] Methylated sncRNAs can be identified by immunoprecipitation with monoclonal/polyclonal antibodies specific for an individual methylation followed by sequencing or hybridization of the separated methylated and unmethylated components. A method combining methylation-specific immunoprecipitation and hybridization directed to specific-sequence identification has been developed by Arraystar™. The miRNA in each of the reaction volumes follows separation of methylated from non-methylated fractions and separate terminal fluorescent labelling (Cy3 and Cy5) for respective fractions. Additional clinical information may be provided by sequencing of the respective methylation fractions. Cheray has compiled various microRNAs that comprise m5C modifications (Cheray 2020). The methylated adenosine can be predicted generally with confidence in the 22-base sequence of microRNA by the presence of the motif sequence DRACH motif (i.e. [AGU][AG]AC[ACU] (Wang, et al. 2021. The m6A consensus motif provides a paradigm of epitranscriptomic studies. Biochemistry, 60(46), pp.3410-3412).

[0096] While the method permits assigning individuals into one or more groups, in some embodiments, an individual identified as a candidate for intervention may be treated by a therapeutic intervention that can prevent, slow, or eliminate the placental bed disorder. Exemplary therapeutic intervention(s) can include any one or more of immunotherapy (e.g., administration of a immunosuppressant and/or anti-inflammatory drug such as intravenous immunoglobulin (IVIG), corticosteroids, Neupogen™, anticoagulant(s) (e.g., heparin(s) such as low molecular weight versions such as Lovenox™), statin(s), progesterone, antibiotic(s), metformin, cervical cerclage, intralipids, “natural” therapies (e.g., omega-3 and/or fish or krill oil preparations, and the like), dietary changes and/or restrictions, bedrest regimens, and the like In some embodiments, the appropriate therapeutic intervention can be selected using various in vitro cell markers of maternal immune cells (any maternal (non-fetal) immune cells or subset thereof, e.g., of peripheral blood mononuclear cells (PBMCs)). Therapeutic intervention, exposures to stress (high body fat, diabetes, high blood pressure, renal disease are exemplary) may result in differential expression within the sncRNA (preferably microRNA, pre- microRNA or pri-microRNA) as quantitative expression or alteration in the epigenetic expression of base modification such as by methylation between pre and post exposure. Different times of interrogation following exposure may result in differences in said differential expression. A time following intervention for sample acquisition, a period of two weeks may be used. Differences in epigenetic modification of sequences in samples acquired before and after an intervention are now recognized. In further embodiments, immune cells are isolated prior to sncRNA (e.g., in some preferred embodiments miRNA) quantification and the specific sncRNAs are quantified within the individual cell types. Isolation may be done by flow cytometry or by paramagnetic beads conjugated to appropriate cell-type selective probes. It is also understood that in situ hybridization of sncRNA (e.g., in some preferred embodiments miRNA) probes may be used for both quantification and identification of the site of expression such as in a tissue, for example a lymph node.

[0097] In some embodiments, quantification of various sncRNAs and patterns of sncRNA (e.g., in some preferred embodiments miRNA) change (e.g., at least one of the sncRNAs) may be listed. SncRNA (e.g., in some preferred embodiments miRNA) expression levels and/or at least one or more equivalent(s) measurement(s) thereof in maternal cells at various time points prior to and following immunotherapeutic intervention may be performed. These sncRNA (e.g., in some preferred embodiments miRNA) “signatures” can direct the clinical assessment and/or treatment. This disclosure also contemplates that the methods, reagents and kits described herein can be used to assess other clinical conditions beyond placental bed disorders and/or different immunotherapeutic interventions. Ratios between a modified canonic or non-canonical sncRNAs (e.g., in some preferred embodiments miRNAs) to the canonic or non-canonic unmodified sncRNAs (e.g., in some preferred embodiments miRNAs) can be calculated. The ratio may be used to assign sample results to one or more treatment groups.

[0098] Additional information may be derived from comparison of differential expression related to specific sncRNAs within the control panel. Differential expression of specific sncRNAs may suggest specific abnormalities in pathways regulated by panel as comprised of microRNAs or other sncRNAs. Thus, differential expression of a sncRNA (e.g., in some preferred embodiments miRNA) within the control panel may suggest an abnormality related to the specific pathway regulated by groups of sncRNAs. It is understood that such findings might suggest specific therapeutic interventions.

[0099] The methods of this disclosure can comprise quantification of one or more individual sncRNAs from the non-placental biological sample and quantifying the individual sncRNAs and comparing the expression levels of sncRNA(s) in the patient sample to the expression levels of the corresponding sncRNA (e.g., in some preferred embodiments miRNA) in a control sample(s). These methods may be modified with the addition of a step which precipitates methylation comprising sncRNAs from those that do not comprise a methylation and wherein the monoclonal/polyclonal antibody utilized is specific for m6A, m5C, m7G or pseudouridine.

[00100] Tunnel Current Sequencing is used by Oxford Nanopore™ to sequence polynucleotides by changes in current flow induced by physical displacement of electrolyte as individual nucleotides of the polynucleotide string passes through a small pore and can be used in the methods of this disclosure. The method does not require modification-specific antibody immunoprecipitation by directly identifying modified bases by a methylated base-specific displacement of electrolyte. The resulting identified modified and unmodified bases can be used to determine the numerator and denominator of the ratio.

[00101] In a preferred embodiment, this disclosure provides methods for treatment for a disorder of pregnancy of a human being, the method comprising: (a) processing patient maternal immune cell sncRNA (e.g., in some preferred embodiments miRNA) to quantify unmethylated patient maternal immune cell sncRNA (e.g., in some preferred embodiments miRNA) and methylated patient maternal immune cell patient sncRNA (e.g., in some preferred embodiments miRNA), if present (b) providing therapeutic intervention to the pregnant human being where the ratio of patient methylated to unmethylated patient maternal immune cell sncRNA (e.g., in some preferred embodiments miRNA) diverges from corresponding control ratio and/or absolute or relative quantity of methylated patient maternal immune cell sncRNA (e.g., in some preferred embodiments miRNA).

[00102] In a preferred embodiment, this disclosure provides methods for treatment for a disorder of pregnancy of a human being, the method comprising: (a) processing patient maternal immune cell sncRNA (e.g., in some preferred embodiments miRNA) to quantify unmethylated patient maternal immune cell sncRNA (e.g., in some preferred embodiments miRNA) and methylated patient maternal immune cell patient sncRNA (e.g., in some preferred embodiments miRNA), and optionally, (b) providing therapeutic intervention to a pregnant human being where the ratio of methylated to unmethylated and/or the absolute or relative quantity of methylated sncRNA (e g., in some preferred embodiments miRNA) in the patient sncRNA (e.g., in some preferred embodiments miRNA) diverges from that of control sncRNA (e.g., in some preferred embodiments miRNA).

[00103] In some preferred embodiments, the ratio and/or absolute and/or relative quantity of the patient sncRNA (e.g., in some preferred embodiments miRNA) diverges from the control sncRNA (e.g., in some preferred embodiments miRNA) or the control ratio by at least about any of 1%, 5%, 10%, 25%, 50%, 100% or more.

[00104] In some preferred embodiments, step (c) comprises comparing the ratio and/or absolute or relative quantity in the patient sncRNA (e.g., in some preferred embodiments miRNA) and the control sncRNA (e.g., in some preferred embodiments miRNA).

[00105] In some preferred embodiments, the control sncRNA (e.g., in some preferred embodiments miRNA) is derived from immune cells of a pregnant human being without a disorder of pregnancy (e.g., of the placental bed) during the first or second trimester of pregnancy.

[00106] In some preferred embodiments, the biological sample comprises mononuclear cells.

[00107] In some preferred embodiments, the biological sample is peripheral blood.

[00108] In some preferred embodiments, the methods further comprise the step of extracting sncRNA (e.g., in some preferred embodiments miRNA)-comprising RNA from the biological sample.

[00109] In some preferred embodiments, the methods include determining a ratio by calculating an HC ratio of expression of said at least one sncRNA (e.g., in some preferred embodiments miRNA), wherein said ratio comprises: a numerator equal to the difference between the mean value of expression of the at least one sncRNA (e.g., in some preferred embodiments miRNA) in the first population and the mean value of the second population and the denominator comprises the average of the two standard deviations of the values for the first and second populations; wherein the at least one sncRNA (e.g., in some preferred embodiments miRNA) is selected from the group consisting of the sncRNAs (e.g., in some preferred embodiments miRNAs) presented herein as being capable of distinguishing the first and second populations. In some preferred embodiments, the first population are compromised pregnancy outcome individuals and the second population is healthy pregnancy outcome individuals. In some preferred embodiments, said sncRNA (e.g., in some preferred embodiments miRNA) exhibits a signal consistency of at least about 85%; a mean signal strength of at least 5.0; and a p value of less than 0.05 (p<0.05). In some preferred embodiments, the at least one sncRNA (e.g., in some preferred embodiments miRNA) exhibits a HC ratio of greater than or equal to about any of 1.0. 1.1, 1.2, 1.3, 1.4, 1.5, 2.0; or greater than or equal to about 3.0.

[00110] In some preferred embodiments, this disclosure provides methods for treating a disorder of pregnancy of a human being, the method comprising: (a) processing patient maternal immune cell sncRNA to physically separate unmethylated patient maternal immune cell sncRNA and methylated patient maternal immune cell sncRNA, if present, (b) quantifying the patient unmethylated and patient methylated maternal immune cell sncRNA, and, optionally, (c) providing therapeutic intervention to the pregnant human being where the ratio of patient methylated to unmethylated patient maternal immune cell sncRNA diverges from corresponding control ratio and/or absolute or relative quantity of methylated patient maternal immune cell sncRNA.

[00111] In some preferred embodiments, this disclosure provides methods for treating a disorder of pregnancy of a human being during the period of 4-10 weeks of pregnancy, the method comprising: (a) separating methylated and unmethylated sncRNA in patient sncRNA derived from immune cells of the pregnant human being, (b) quantifying the amount of each of the methylated and unmethylated sncRNA in the patient sncRNA, and (c) providing therapeutic intervention to a pregnant human being where the ratio of methylated to unmethylated and/or the absolute or relative quantity of methylated sncRNA in the patient sncRNA diverges from that of control sncRNA.

[00112] In some preferred embodiments of such methods, the ratio and/or absolute and/or relative quantity of the patient sncRNA diverges from the control sncRNA or the control ratio by at least about any of 10%, 25%, 50%, 100% or more; step (c) comprises comparing the ratio and/or absolute or relative quantity in the patient sncRNA and the control sncRNA; the control sncRNA is derived from immune cells of a pregnant human being without a disorder of the placental bed during the first or second trimester of pregnancy; the control sncRNA is derived from immune cells of a pregnant human being without a disorder of the placental bed during the first or second trimester of pregnancy; the biological sample comprises mononuclear cells; the biological sample is peripheral blood; the method comprises extracting sncRNA-comprising RNA from the biological sample; the method comprises calculating a ratio of expression of said at least one sncRNA (e.g., in some preferred embodiments miRNA), wherein said ratio comprises a numerator equal to the difference between the mean value of expression of the at least one sncRNA in the first population and the mean value of the second population and the denominator comprises the average of the two standard deviations of the values for the first and second populations, wherein the at least one sncRNA is selected from the group consisting of the sncRNAs presented herein as being capable of distinguishing the first and second populations; the first population are compromised pregnancy outcome individuals and the second population is healthy pregnancy outcome individuals; the sncRNA exhibits a signal consistency of at least about 85%, a mean signal strength of at least 5.0, and a p value of less than 0.05 (p<0.05); and/or, the at least one sncRNA exhibits a HC ratio of greater than or equal to about any of 1.0. 1.1, 1.2, 1.3, 1.4, or 1.5; or greater than or equal to about 1.3 and/or the patient ratio diverges by a standard deviation of about 1, 1.5, 2.0 from the mean of the control group.

[00113] In some preferred embodiments, this disclosure provides methods wherein the quantifying step (e.g., step (b)) comprises: binding a target microRNA of a biological sample with a capture probe complementary to the mature microRNA sequence attached to a solid phase, the target microRNA labeled with a first fluorophore; incubating an antibody having binding specificity for methylated RNA, the antibody comprising a second fluorophore distinct from the first fluorophore; and, calculating the difference of fluorescence of the first and second fluorophores and the second fluorophore to determine the fraction of the microRNA in the biological sample that is methylated. Exemplary embodiments are presented in Figs. 5A and 5B.

[00114] In the preferred embodiment illustrated in Fig. 5A, an unlabeled “capture” probe (probe 1) complementary to the mature microRNA is attached to the solid phase. The target RNA (mature microRNA) is end-labeled with a spectrally distinct fluorophore (probe 3). An antibody such as anti- m6A: (cat. #202 003 Synaptic Systems, Goettingen, Germany, for example) is end-labelled with a spectrally distinct fluorophore (probe 2). Probe 2 comprising the antibody is incubated with the RNA captured by the unlabeled probe 1. Fluorescent signals from binding of probe 2 represent the fluorescence due to the presence of the methylation, e.g., m6A. The fluorescence signal from the captured RNA (probe 3) represents the fluorescence of both the methylated and unmethylated fractions of the target RNA. The ratio of signal 2 to signal 3 represents the fraction of the microRNA that is methylated.

[00115] In the preferred embodiment illustrated in Fig. 5B, pre-microRNA is shown as a “hairpin” (containing a loop structure) with the two constituents of the mature sequence (Probe 1) hybridized together. The hybridized strands together constitute the mature sequences joined together by the loop sequence constituting the pre-microRNA. Probe 1 (green) is complementary to one strand of the hybridized pair and is of sufficient length to hybridize under the selected conditions of hybridization (namely below the melting temperature of the two strands). The second probe (probe 2-blue) is complementary to a portion of the mature sequence but of insufficient length to hybridize with the structure under the selected hybridizing conditions. The second probe is also complementary to a contiguous length of the loop structure such that it is also of insufficient length to bind under the selected hybridizing conditions but together with the portion of the probe complementary to the mature sequence will hybridize. Probes 1 and 2 are labelled with spectrally distinct fluorophores. After hybridizing, the reaction wells are washed free of unbound probe and fluorescence quantified. Precise quantification of the mature sequence is the difference between the signal derived from probe 1 less the corresponding signal from probe 2.

[00116] Other methods known in the art may also be used such as that described by Liu et al. (Liu, Y.; Zhang, H. RNA m6A Modification Changes in Postmortem Nucleus Accumbens of Subjects with Alcohol Use Disorder: A Pilot Study. Genes 2022, 13, 958). This and other exemplary methods are described in the Examples section herein.

[00117] Other aspects and embodiments, including useful variations thereof, are also disclosed and/or contemplated herein as will be understood by those of skill in the art.

[00118] All patents and references whether conventionally cited in the literature or addressed through internet links herein are incorporated in entirety by reference. All technical and scientific terms used within this description shall have the same meaning as commonly understood by those or ordinary skill in the art disclosed herein except where otherwise specifically defined. Following longstanding practice patent law conventions, the terms “a’, and “the” refer to “one or more” when used in this application including the claims. Thus, for example, reference to “a polynucleotide” includes a plurality of such polynucleotides, and so forth. [00119] EXAMPLES

[00120] Specimen collection and selection of time during gestation: a. Collect maternal blood specimens from pregnant women in the first trimester of pregnancy in two groups: controls (those destined to healthy pregnancy) and patient specimens (those destined to a placental bed disorder) which are to be compared with control specimens. b. Other time points may be selected based on clinical need as defined by clinician (e.g. from 4-6 weeks pregnant or later time points into second trimester up to 20 weeks so long as patient and control specimen collection time points are the same).

[00121] Specimen handling: Use one tube containing EDTA or heparin, 7ml or greater

[00122] Specimen handling and preparation: Collect and hold at room temperature or (preferably at 4°C) preferably no longer than 24 hours; select type of sample prep desired: c. Whole blood: prepare blood using the PAXgene system following the manufacturer’s directions and transfer to cryotube and freeze in liquid nitrogen vapor phase until use. d. PBMC prepared as per description e. Buffy coat prepared as per description f. Whole blood

[00123] MIcroRNA quantification: Quantification of Mature microRNA using hybridization in an ELISA format

[00124] Detection and quantification by hybridization is problematic. While sequencing permits direct and unambiguous quantification of the mature microRNA sequence, hybridization detects the presence of the sequence by itself or within a larger sequence in which the target sequence is embedded such as pre-microRNA. Various methods of attachment of the target RNA strand to the solid phase are contemplated. It may be through non-specific binding. However, the binding may be in competition with irrelevant RNA, reducing the binding of the specific RNA.

[00125] Two probes (RNA or DNA) are constructed. Each are end-labeled with spectrally distinct fluorophores wherein the two probes are reacted in a single reaction volume or they may be run using a single fluorophore in separate reaction volumes preventing competition. The first probe (probe 1) is complementary only to the mature sequence and stably binds to the mature sequence under the selected hybridization conditions (just below the melting temperature of the probe and targe RNA). The second probe (probe 2) is configured to bind to a portion of the mature microRNA (complementary) and to a portion of the loop sequence of the pre-microRNA where the two sequences form a continuous sequence. The lengths of each of these segments alone are insufficient to stably hybridize to the target RNA under the hybridizing conditions used with probe 1. Wherein both sequences are present as in pre-microRNA, probe 2 hybridizes with the target RNA under the selected hybridization conditions. The signals derived from probe 1 less that of probe 2 represents the signal derived from hybridization with the mature sequence alone.

[00126] Procedure for quantification of the methylated microRNA illustrated in Figure 5A

[00127] An unlabeled “capture” probe (probe 1) complementary to the mature microRNA is attached to the solid phase (Figure 5A). The target RNA (mature microRNA) is end-labeled with a spectrally distinct fluorophore (probe 3). An antibody such as anti-m6A: (cat. #202 003 Synaptic Systems, Goettingen, Germany, for example) is end-labelled with a spectrally distinct fluorophore (probe 2). Probe 2 comprising the antibody is incubated with the RNA captured by the unlabeled probe 1. Fluorescent signals from binding of probe 2 represent the fluorescence due to the presence of the methylation, e.g. m6A. The fluorescence signal from the captured RNA (probe 3) represents the fluorescence of both the methylated and unmethylated fractions of the target RNA. The ratio of signal 2 to signal 3 represents the fraction of the microRNA that is methylated.

[00128] Discrimination of mature and pre-microRNA by method illustrated in Figure 5B

[00129] Pre-microRNA is shown as a “hairpin” (containing a loop structure) with the two constituents of the mature sequence (Probe 1) hybridized together (Figure 5B). The hybridized strands together constitute the mature sequences joined together by the loop sequence constituting the pre- microRNA. Probe 1 (green) is complementary to one strand of the hybridized pair and is of sufficient length to hybridize under the selected conditions of hybridization (namely below the melting temperature of the two strands). The second probe (probe 2 -blue) is complementary to a portion of the mature sequence but of insufficient length to hybridize with the structure under the selected hybridizing conditions. The second probe is also complementary to a contiguous length of the loop structure such that it is also of insufficient length to bind under the selected hybridizing conditions but together with the portion of the probe complementary to the mature sequence will hybridize. Probes 1 and 2 are labelled with spectrally distinct fluorophores. After hybridizing, the reaction wells are washed free of unbound probe and fluorescence quantified. Precise quantification of the mature sequence is the difference between the signal derived from probe 1 less the corresponding signal from probe 2.

[00130] MIcroRNA quantification, method #2: Quantification of microRNAs and their methylated components using a method similar to Lui (Genes, 2022)

[00131] The following laboratory procedures, the preferred embodiment, detailed below, can be used as shown by Liu et al. (Liu, Y. ; Zhang, H. RNA m6A Modification Changes in Postmortem Nucleus Accumbens of Subjects with Alcohol Use Disorder: A Pilot Study. Genes 2022, 13, 958) and detailed below.

[00132] Validation of procedure: Liu generates useful data following the preferred embodiment below as detailed in the referenced paper (Liu).

[00133] The assay quantifies sncRNAs (for example, sncRNAs) by sequence hybridization against complementary sequences where the modified and unmodified sncRNA variants are discretely quantified. The method below separates labeled and unlabeled fractions into separate reaction volumes wherein they are separately labeled with distinguishable markers permitting their separate quantification. In this process, the sncRNAs are reacted with an antibody, such as a rabbit polyclonal specific for the modification of choice, namely m6A. The immune complex formed is reacted with Dynabeads that are functionalized with an antibody directed against the rabbit antibody followed by isolation of the complex from the unlabeled fraction by magnetic separation of the superparamagnetic Dynabeads resulting in two separate reaction volumes, one comprising the modified and the second the unmodified sncRNAs. sncRNAs within each reaction volume are end labeled with distinguishable markers such as Cy5 and Cy3. The volumes are mixed and hybridized to sncRNA-complementary sequences attached to a solid phase and the fluorescence of the individually labeled sncRNAs quantified.

[00134] Exemplary, Preferred Procedure

[00135] Blood is drawn from pregnant women in their first or second trimester in an anti coagulated tube (EDTA). Specimens are maintained at room temperature for no longer than 24 hours after collection. Buffy coat is prepared by centrifugation at about 800xg for 10 minutes at room temperature. The white/tan-colored interface identified between the acellular supernatant fluid and red cell mass is collected and transferred to a sealable tube suitable for freezing at -80°C and maintained frozen until testing.

[00136] Thawing of the buffy coat is performed in an ice bath. Total RNA is isolated using standard techniques, preferably using the RNeasy Mini Kit (Qiagen) following the manufacturer’s instructions. (Cat no. / ID. 73404 URL: https://www.qiagen.com/ last accessed April 9 2025). RNA integrity and concentration were measured using the Agilent 2100 Bioanalyser with the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA, USA).

Total RNA yield is assessed using the absorbance at 260 nm and the ratio of 260/280 and 260/230, and RNA integrity assessed with Agilent's Bioanalyzer NANO Lab-on-Chip instrument (Agilent Technologies, (URL: https://www.agilent.com/en/product/automated-electrophoresis/bioanalyzer- systems/bioanalyzer-ma-kits-reagents/bioanalyzer-small-rna-analysis-228257, last accessed April 9, 2025). All steps are performed according to the manufacturer’s instructions.

[00137] Immunoprecipitation of m6A methylated sncRNA

[00138] 1-3 pg total RNA and optionally with m6A spike-in control mixture are added to 300 pL of IP buffer (50 mM Tris-HCl, pH7.4, 150 mM NaCl, 0.1% NP40, 40 U/pL RNase Inhibitor) containing 2 pg of anti-m6A rabbit polyclonal antibody (cat. #202 003 Synaptic Systems, Goettingen, Germany). Reaction is incubated with head-over-tail rotation at 4 °C for 2 hours (or following Agilent’ s “miRNA Complete Labeling & Hybridization Kit number 5190-0456” kit following the manufacturer’s instructions).

[00139] Subsequently, 20 pL of Dynabeads M-280 Sheep Anti-Rabbit IgG suspension (Invitrogen, Waltham, MA, USA); https://www. thermofisher.com/order/ catalog/product/11203D) are added and the reaction is incubated with head-over-tail rotation at 4°C for 2 hours 20 pU of Dynabeads per sample. This is then blocked with freshly prepared 0.5% bovine serum albumin (BSA) at 4°C for an additional 2 hours, washed three times with 300 pL of IP buffer, and then washed three times with head-over-tail rotation at 4 °C for an additional 2 hours. The beads are then washed three times more in 500 pL of IP buffer and twice with 500 pL wash buffer (50 mM Tris-HCl, pH7.4, 50 mM NaCl, 0.1% NP40, 40 U/pL RNase Inhibitor). The immunoprecipitated (IP) fraction is then bound to the Dynabeads with the captured m6A-modified RNA. This is then eluted using the elution buffer for Dynabeads comprised in the purification kit using wash buffer ((50 mM Tris-HCl, pH7.4, 50 mM NaCl, 0.1% NP40, 40 U/pL RNase Inhibitor (Invitrogen, cat # 61006). The supernatant (Sup) fraction containing the m6A-unmodified RNA is then recovered from the centrifuged supernatant per the manufacturer’s instructions. The enriched RNA is then eluted with 200 pL (Elution buffer (10 mM Tris-HCl, pH7.4, 1 mM EDTA, 0.05% SDS, 40 U Proteinase K) at 50°C for 1 hour. The RNA is then extracted by acid phenol -chloroform and ethanol precipitated.

[00140] Thus, two volumes of sncRNA are created, one m6A methylated and the second unmethylated. They are labeled Cy3 (for “Sup”) and Cy5 (for “IP”). The IP and Sup fractions are separately labeled with fluorescent dyes (C5 and C3 for IP and Sup, respectively) per manufacturer’s instructions (miRNA Complete Labeling and Hybridization, (Agilent Technologies, cat. #5190-0456).

[00141] Hybridization to microarray slide

[00142] Spike-in control is added optionally to the labeled mixture of the Cy5 and Cy3 labeled volumes performed by using Agilent’s “miRNA Spike-In Kit, number 5190-1934” prior to hybridization. The mixture is then hybridized to appropriate microarray (SurePrint Human miRNA Microarray, number cat. G4872A-070156, Release 21.0, 8 x 60K” (URL: https ://www. agilent. com/store/en US/Prod-G4872A-070156/G4872A-070156) by following instructions in Agilent’s “miRNA Complete Labeling & Hybridization Kit number 5190-0456.” The slides are then incubated at 65 °C for 17 hours in an Agilent Hybridization Oven (Agilent Technologies, Santa Clara, CA, USA) following manufacturer’s instructions (URL: https://www.agilent.com/cs/librarv/usermanuals/public/G2545-90001 HybOven A.pdf last accessed April 15, 2025).

[00143] SncRNA Cy5 and Cy3 fluorescent intensities are then measured using Agilent’s. Agilent’s G5761A “SureScan Dx Microarray Scanner System ” according to manufacturer’s instructions (URL: https://www. agilent. com/cs/library/usermanuals/public/GEN-MAN-G5761- 90003.pdf last accessed April 10, 2025). Image files are exported from the scanner as “TIF” files” and imported into a computer running Agilent Feature Extraction Software (12.2.0.7) following software manual instructions, (URL: https://www.agilent.com/cs/librarv/usermanuals/public/G4460-

90064.pdf last accessed 4/15/25/ The image file data are converted into a data file format (e.g. Excel) so that the Pregnancy Risk Score Panel can subsequently be developed using the quantifications. [00144] Development of Pregnancy Risk Score Panel: HC ratio calculations

[00145] After sncRNA values are processed into a data file, an “HC ratio” is then calculated for each listed sncRNA. The HC ratio is calculated by taking the absolute value of the difference between mean sncRNA level of the test samples (healthy pregnancies) minus the mean sncRNA values of the control samples (unhealthy pregnancies) as the numerator then divided by the average of the standard deviations of the two groups (healthy and unhealthy pregnancies) as the denominator. Note: For scoring purposes, methylated sncRNAs are classified as separate sncRNAs (e.g, the methylated version miR-155-5p is labeled as a different sncRNA from the non-methylated version of miR-155-5p) as each has its own individual predictor ability. After HC ratios are calculated for each sncRNA, the HC ratio results are then sorted from the highest to lowest sncRNA. The sncRNAs identified with high HC ratios (>1.5) have a greater ability to distinguish between the two population groups. These top sncRNAs with high HC ratios (>1.5) are then selected for the sncRNA Risk Score Panel (see detailed example of a similar ratio sorting procedure described in Winger/ Reed patent application PCT /US2012/061994, published 25, October 2012 (25.10.2012)).

[00146] SncRNA selection

[00147] As explained earlier, a preliminary sorting of sncRNAs is performed by sorting their “HC ratios from the highest to lowest. These sncRNAs identified with high HC ratios (>1.5) are selected for use in the Pregnancy Risk Score Panel.

[00148] Developing a Risk Score Panel

[00149] To develop a Pregnancy Risk Score system, a scoring system is constructed based on the top sncRNAs selected. First, a ROC curve analysis for pregnancy outcome prediction is performed for each selected sncRNA (HC ratio>1.5). The Youden Index J Associated Criterion Value point can be calculated for each sncRNA using standard AUC-ROC statistical software (e.g. Medcalcl Statistical software version 19.0.7 Ostend, Belgium). This Youden Index J Associated Criterion Value point determines the sncRNA’ s positive/negative cut-off for pregnancy outcome prediction. In this scoring system, sncRNA values greater or equal than their “cut-off’ value is assigned a score of “1”. sncRNA value less than the cut-off value is assigned a score of “0”. Score points for each selected sncRNA are then added together to determine a sample’s total Risk Score for adverse pregnancy outcome (See similar scoring procedure ref Winger EE, Reed IL et al. PLoS One. 2020 Aug 13; 15(8):e0236805. doi: 10.1371/joumal. pone.0236805). As explained earlier, the methylated version of each named sncRNA is treated as a separate sncRNA to the non-methylated version of the same named for scoring purposes (e.g. methylated miR155-5p is scored separately from non-methylated miR155-5p). After ROC curve calculations for individual sncRNAs have been completed, then an AUC-ROC calculation for the Risk Score Panel (selected sncRNAs combined together) can be calculated for the population. First, the individual Risk Scores and outcomes for each sample (e.g. “Healthy” or “Unhealthy”) are entered into standard statistical analysis software for ROC curve analysis (e.g. MedCalc® Statistical Software version 19.6.4 (MedCalc Software Ltd, Ostend, Belgium; https://www.medcalc.org; 2021). If the AUR- ROC curve is greater than 0.80 then the Risk Score Test is deemed predictive for the adverse pregnancy outcome. The sensitivity/ specificity at the AUC-ROC cut-off calculated by the software. The Youden Index J Criterion Value Point of the AUC-ROC determines the predictive value of the test.

[00150] Application of Pregnancy Risk Score Panel to an individual patient to assess risk

[00151] The Youden Index J Associated Criterion Values derived for each of the selected sncRNAs contained in the Pregnancy Risk Panel are used as its positive/negative cut-off for the test used on an individual patient. sncRNA values that exceed the cut-off value for each of the sncRNAs in the Pregnancy Risk panel are assigned a point value of “1”. sncRNA values that do not exceed the cut-off value are assigned a point value of “0”. The sum of the points for each sncRNA in the Pregnancy Risk Panel equals the patient’s “Total Pregnancy Risk Score”. The chance of adverse pregnancy outcome based on the “Risk Score” is determined by the sensitivity/ specificity at the AUC-ROC cut-off value calculated for Pregnancy Risk Panel’s Youden Index J Criterion Value Point. If the Risk Score for a woman’s blood sample exceeds the cut-off value (Youden point) calculated, then the woman is more likely to experience the named adverse pregnancy outcome of interest. The sensitivity/ specificity determines the statistical chance that the patient will have adverse pregnancy outcome based on the test’s ROC calculation. To see an example of a similar sncRNA pregnancy risk ROC scoring method applied to real patient samples, see: ref Winger, et al. 2020 Aug 13; 15(8):e0236805. doi: 10.1371/joumal.pone.0236805. PMID: 32790689; PMCID: PMC7425910.

[00152] In summary, the described herein are presently preferred embodiments. However, one skilled in the art that pertain to this disclosure will understand that the principles of this disclosure can be extended easily with appropriate modifications to other applications.

[00153] Adding methylated sncRNA markers may enable faster treatment response times [00154] As seen in published papers by Winger and Reed, certain sncRNAs react predictably (increase or decrease levels) in response to certain therapies, especially pregnancy therapies such as IVIG (intravenous gamma globulin). (Winger EE, ReedJL,. J Reprod Immunol. 2015 Aug; 110:22-35. doi: 10.1016/j.jri.2015.03.005, Patent WO 2013/063322), because of this, sncRNA signatures can be used to monitor treatment response in early pregnancy. For example, a sncRNA Pregnancy Risk Panel score falling above or below a set “cut-off’ value may determine the risk of a poor pregnancy outcome without a certain therapy, such as IVIG. If a patient’s sncRNA Pregnancy Risk Score surpasses the “cut-off’ based on ROC curve analysis, interventional treatment, (e.g. IVIG) may be recommended. The physician may then elect to retest the patient’ s levels again to see if sncRNA levels have normalized post-therapy to a score that indicates less risk. The addition of the highly responsive methylated sncRNA to the sncRNA panel likely s adds more responsiveness to the therapy response time, improving the ability of physicians to assess interventions in a timely manner. The addition of methylation markers, therefore, will likely improve the clinical usefulness of sncRNA test.

[00155] Tables. The tables shown below are to be understood with reference to the description of each provided below.

[00156] Table 1. 175 microRNAs were ranked in order from highest to lowest expression level for each racial group. From these ranking levels, R1R2 calculation was determined for each microRNA to assess those most differentially ranked between black and non-black populations. The R1R2 value for each microRNA consists of the following calculation: the difference between the mean microRNA ranking level of one race group (black population, “Race group 2”) minus the mean microRNA level for the other racial group (non-black population, “Race group 1”) as the numerator and the denominator is determined by calculating the average of the two standard deviations for Race group 1 and Race group 2. The absolute value of this ratio is calculated. MicroRNAs are then sorted from highest to lowest R1R2 value to determine the top microRNAs. The top twenty microRNAs with the highest R1R2 values determined. These top twenty microRNAs were later confirmed to be statistically significant using multiple test methods, including the Mann Whitney U test and the Kolmogorov - Smirnov test (p value<0.05 in all cases. Software: NC.S'k statistical software, version 2023, NCSS, LLC, Kaysville, Utah, USA). Table 1

[00157] Table 2. Pregnancy outcome predictor microRNAs demonstrating mature microRNA methylation (m6A). Mature microRNAs known to contain m6A methylations with pregnancy outcome prediction potential are listed. MicroRNAs with pregnancy outcome prediction ability are given in Columns I. Column C lists data from Table 2. The top 5% (65/1,240) microRNAs demonstrating the most differential expression (delta A) between healthy and unhealthy pregnancy in the first trimester with two sequential blood draws (7 and 11 weeks), in other words, the top 5% microRNAs most increased in healthy pregnancy that are also the top 5% most decreased in unhealthy pregnancy. Column D identifies top pregnancy outcome prediction microRNAs from our previous Winger/Reed patents and publications (reference sources in Column E). Lastly, Column I lists mature microRNAs known to contain m6Amethylations from the literature. Columns F, G and H list known biological consequences of these methylations from published references.

Table 2

References referred to in Table 2:

Patent A: European Pat No, 2,771,498 Bi granted on January 17, 2018 entitled METHODS ANB COMPOSITIONS TOP ASSESSING PATIENTS WITH REPRODUCTIVE FAILURE USING IMMUNE CELL-DERIVED MICRORN A. naming Inventors Edward A. Winger and Jane L. Reed [00158] Table 3. KEGG pathways associated the most differentially expressed microRNAs between blacks and non-black populations in peripheral blood immune cells taken from the first trimester of healthy pregnancy. Top KEGG pathways associated the top 20 most differentially expressed microRNAs between Healthy pregnant blacks and non-blacks in the first trimester of pregnancy (Table 2). The most statistically significant pathways were found to be MAPK signaling pathway (hsa04010), PI3K-Akt signaling pathway (hsa04151), Prostate cancer (hsa05215), MAPK signaling pathway(hsa04010) and Focal adhesion (hsa04510). These pathways are associated with growth, proliferation and migration.

Table 3

** Pathway analysis performed using DIANA mjRPath v.2.0: (Web server issue. lasty accessed Feb 2023).

[00159] Table 4. Maternal PBMC microRNA level change measured for two sequential blood draws in the first trimester of pregnancy. Maternal PBMC microRNA level change (“Delta” A) was measured for two sequential blood draws in the first trimester of pregnancy using of three individual patients (two with Preterm birth and one with a healthy full- term delivery). Mean gestational age of first blood draw was 56.7 days pregnant (7.2 weeks gestational age) and the mean gestational age of the second blood draw was 74.3 days pregnant (10.6 weeks gestational age). MicroRNAs are sorted by largest difference between Preterm pregnancy miRNA delta A (^increasing) and Healthy pregnancy miRNA delta A (j, decreasing) (Column F). Approximately 2,400 microRNAs contained in the microarray, of which 1,240 showed measurable signal. The top 1% most differentially expressed between Healthy and Preterm were considered most clinically useful for pregnancy outcome prediction.

Table 4

[00160] Table 5. Features of top marker-candidate MicroRNAs are compared. The table demonstrates microRNAs that share significant features: Column B: xx = Member of Twenty MicroRNA group with largest differences in expression levels between first trimester Black and nonBlack patients, Column C: x = microRNA that express most diminishing (bottom 2.4% of 1,240 microRNAs) expression levels in first trimester pregnant patient sequential blood draws with healthy outcomes. Column D: x = MicroRNA that express most increasing (top 2.4% of 1,240 microRNAs) expression levels in first trimester pregnant patients sequential blood draws with Unhealthy outcomes as seen in Table 3 above. Column E: x=Top microRNAs associated with 24 genes regulated by a common Neanderthal introgressed enhancer SNP Gene Ontology set “Regulation of Cell locomotion,” Gene ontology: G0:0040012 (Ref: Neanderthal gene information resource: Supplementary file entitled “Document S8. Table S7” from Silvert M, Quintana-Murci L, Rotival M. Impact and Evolutionary Determinants of Neanderthal Introgression on Transcriptional and Post-Transcriptional Regulation. Am J Hum Genet. 2019 Jun 6;104(6):1241-1250. doi: 10.1016f.ajhg.2019.04.016. Epub 2019 May 30. PM1D: 31155285; PMCID: PMC6557732.)

Table 5

Table 5 key: A) microRNA; B) Top 20 "race identifying” microRNAs in first trimester (Black vs NonBlack); C) Top 2.4% decreasing microRNA expression levels in 1^st trimester Healthy Pregnancy; D) Top 2.4% most increasing microRNA expression levels in 1^st trimester Unhealthy pregnancy; and, E) Neanderthal introgressed enhancer SNP gene related microRNAs.

[00161] Table 6. Top 35 microRNAs that regulate nine m6A “eraser” ALKBH5 genes in early pregnancy. Top 35 MicroRNAs that regulate nine genes: SMAD1, SMAD2, SMAD3, SMAD5, ALKBH5, MMP9, ITGA1, HIF1A, and TGFB1. Top 35 microRNAs were calculated using the Mirdip online tool (MirDip online analysis tool: Tokar T, et al. mirDIP 4.1 -integrative database of human microRNA target predictions. Nucleic Acids Res. 2018 Jan 4;46(Dl):D360-D370. doi: 10.1093/nar/gkxll44. PubMed PM1D: 29194489; PubMed Central PMC1D: PMC5753284). Nine genes were selected from the reference: Zheng Q, Yang F, Gan H, Jin L. Hypoxia induced ALKBH5 prevents spontaneous abortion by mediating m6A-demethylation of SMAD1/5 mRNAs. Biochim Biophys Acta Mol Cell Res. 2022 Oct; 1869(10) : 119316as. Table 6

[00162] All documents cited in this disclosure are hereby incorporated into this disclosure in their entirety. While the present inventions of this disclosure have been described in terms of the preferred embodiments, it is understood that variations and modifications will occur to those skilled in the art. Therefore, it is intended that the appended claims cover all such equivalent variations that come within the scope of the inventions as claimed.

Claims

CLAIMS What is claimed is:

1. A method of treatment for a disorder of pregnancy of a human being, the method comprising:

(a) processing patient maternal immune cell sncRNA to physically separate unmethylated patient maternal immune cell sncRNA and methylated patient maternal immune cell sncRNA, if present,

(b) quantifying the patient unmethylated and patient methylated maternal immune cell sncRNA, and

(c) providing therapeutic intervention to the pregnant, human being where the ratio of patient methylated to unmethylated patient maternal immune cell sncRNA diverges from corresponding control ratio and/or absolute or relative quantity of methylated patient maternal immune cell sncRNA.

2. A method of treatment of disorder of pregnancy of a human being during the period of 4-10 weeks of pregnancy, the method comprising:

(a) separating methylated and unmethylated sncRNA in patient sncRNA derived from immune cells of the pregnant human being,

(b) quantifying the amount of each of the methylated and unmethylated sncRNA in the patient sncRNA, and

(c) providing therapeutic intervention to a pregnant human being where the ratio of methylated to unmethylated and/or the absolute or relative quantity of methylated sncRNA in the patient sncRNA diverges from that of control sncRNA.

3. The method of any one of claims 1-2 wherein the ratio and/or absolute and/or relative quantity of the patient sncRNA diverges from the control sncRNA or the control ratio by at least about any of 10%, 25%, 50%, 100% or more.

4. The method of any one of claims 1-3 wherein step (c) comprises comparing the ratio and/or absolute or relative quantity in the patient sncRNA and the control sncRNA.

5. The method of any one of claims 1-4 wherein the control sncRNA is derived from immune cells of a pregnant human being without a disorder of the placental bed during the first or second trimester of pregnancy.

6. The method of claim 5 wherein the control sncRNA is derived from immune cells of a pregnant human being without a disorder of the placental bed during the first, or second trimester of pregnancy.

7. The method of any one of claims 1-6 wherein the biological sample comprises mononuclear cells.

8. The method of any preceding claim wherein the biological sample is peripheral blood.

9. The method of claim 8, further comprising the step of extracting sncRNA-comprising RNA from the biological sample.

10. The method of any preceding claim further comprising calculating a ratio of expression of said at least one sncRNA, wherein said ratio comprises: a numerator equal to the difference between the mean value of expression of the at least one sncRNA in the first population and the mean value of the second population and the denominator comprises the average of the two standard deviations of the values for the first and second populations; wherein the at least one sncRNA is selected from the group consisting of the sncRNAs presented herein as being capable of distinguishing the first and second populations.

11. The method of claim 10 wherein the first population are compromised pregnancy outcome individuals and the second population is healthy pregnancy outcome individuals.

12. The method of ciaim 11 or 12 wherein said sncRNA exhibits a signa! consistency of at least about 85%; a mean signal strength of at least 5.0; and a p value of less than 0.05 (p<0.05).

13. The method of any one of claims 10-12 wherein the at least one sncRNA exhibits a HC ratio of greater than or equal to about any of 1.0. 1.1, 1.2, 1.3, 1.4, or 1.5; or greater than or equal to about 1.3 and/or the patient ratio diverges by a standard deviation of about 1, 1.5, 2.0 from the mean of the control group.

14. The method of claim 1 or 2 wherein step (b) comprises:

1) binding a target microRNA of a biological sample with a capture probe complementary to the mature microRNA sequence attached to a solid phase, the target microRNA labeled with a first fluorophore;

2) incubating an antibody having binding specificity for methylated RNA, the antibody comprising a second fluorophore distinct from the first fluorophore;

3) calculating the difference of fluorescence of the first and second fluorophores and the second fluorophore to determine the fraction of the microRNA in the biological sample that is methylated.