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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
<journal-title>Frontiers in Genetics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Genet.</abbrev-journal-title>
<issn pub-type="epub">1664-8021</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1215472</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2023.1215472</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Genetics</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Placental microRNA methylome signatures may serve as biomarkers and therapeutic targets for prenatally opioid-exposed infants with neonatal opioid withdrawal syndrome</article-title>
<alt-title alt-title-type="left-running-head">Radhakrishna et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fgene.2023.1215472">10.3389/fgene.2023.1215472</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Radhakrishna</surname>
<given-names>Uppala</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1508337/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Nath</surname>
<given-names>Swapan K.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/52490/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Uppala</surname>
<given-names>Lavanya V.</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Veerappa</surname>
<given-names>Avinash</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Forray</surname>
<given-names>Ariadna</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Muvvala</surname>
<given-names>Srinivas B.</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2049275/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Metpally</surname>
<given-names>Raghu P.</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Crist</surname>
<given-names>Richard C.</given-names>
</name>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Berrettini</surname>
<given-names>Wade H.</given-names>
</name>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
<xref ref-type="aff" rid="aff8">
<sup>8</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2026320/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Mausi</surname>
<given-names>Lori M.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Vishweswaraiah</surname>
<given-names>Sangeetha</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1504830/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bahado-Singh</surname>
<given-names>Ray O.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine</institution>, <addr-line>Royal Oak</addr-line>, <addr-line>MI</addr-line>, <country>United States</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Arthritis and Clinical Immunology Program</institution>, <institution>Oklahoma Medical Research Foundation</institution>, <addr-line>Oklahoma City</addr-line>, <addr-line>OK</addr-line>, <country>United States</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>College of Information Science and Technology</institution>, <institution>Peter Kiewit Institute</institution>, The University of Nebraska at Omaha, <addr-line>Omaha</addr-line>, <addr-line>NE</addr-line>, <country>United States</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Genetics, Cell Biology and Anatomy College of Medicine</institution>, <institution>University of Nebraska Medical Center</institution>, <addr-line>Omaha</addr-line>, <addr-line>NE</addr-line>, <country>United States</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Department of Psychiatry, Yale School of Medicine</institution>, <addr-line>New Haven</addr-line>, <addr-line>CT</addr-line>, <country>United States</country>
</aff>
<aff id="aff6">
<sup>6</sup>
<institution>Department of Molecular and Functional Genomics</institution>, <addr-line>Danville</addr-line>, <addr-line>PA</addr-line>, <country>United States</country>
</aff>
<aff id="aff7">
<sup>7</sup>
<institution>Department of Psychiatry, University of Pennsylvania Perelman School of Medicine</institution>, <addr-line>Philadelphia</addr-line>, <addr-line>PA</addr-line>, <country>United States</country>
</aff>
<aff id="aff8">
<sup>8</sup>
<institution>Geisinger Clinic</institution>, <addr-line>Danville</addr-line>, <addr-line>PA</addr-line>, <country>United States</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/677047/overview">Hong Ji</ext-link>, University of California, United States</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1116914/overview">Elizabeth Yen</ext-link>, Tufts University, United States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1666742/overview">Xiguang Xu</ext-link>, Virginia Tech, United States</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Uppala Radhakrishna, <email>Uppalar99@gmail.com</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>15</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1215472</elocation-id>
<history>
<date date-type="received">
<day>02</day>
<month>05</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>01</day>
<month>06</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Radhakrishna, Nath, Uppala, Veerappa, Forray, Muvvala, Metpally, Crist, Berrettini, Mausi, Vishweswaraiah and Bahado-Singh.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Radhakrishna, Nath, Uppala, Veerappa, Forray, Muvvala, Metpally, Crist, Berrettini, Mausi, Vishweswaraiah and Bahado-Singh</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>
<bold>Introduction:</bold> The neonate exposed to opioids in utero faces a constellation of withdrawal symptoms postpartum commonly called neonatal opioid withdrawal syndrome (NOWS). The incidence of NOWS has increased in recent years due to the opioid epidemic. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in gene regulation. Epigenetic variations in microRNAs (miRNAs) and their impact on addiction-related processes is a rapidly evolving area of research.</p>
<p>
<bold>Methods:</bold> The Illumina Infinium Methylation EPIC BeadChip was used to analyze DNA methylation levels of miRNA-encoding genes in 96 human placental tissues to identify miRNA gene methylation profiles as-sociated with NOWS: 32 from mothers whose prenatally opioid-exposed infants required pharmacologic management for NOWS, 32 from mothers whose prenatally opioid-exposed infants did not require treat-ment for NOWS, and 32 unexposed controls.</p>
<p>
<bold>Results:</bold> The study identified 46 significantly differentially methylated (FDR <italic>p</italic>-value &#x2264; 0.05) CpGs associated with 47 unique miRNAs, with a receiver operating characteristic (ROC) area under the curve (AUC) &#x2265;0.75 including 28 hypomethylated and 18 hypermethylated CpGs as potentially associated with NOWS. These dysregulated microRNA methylation patterns may be a contributing factor to NOWS pathogenesis.</p>
<p>
<bold>Conclusion:</bold> This is the first study to analyze miRNA methylation profiles in NOWS infants and illustrates the unique role miRNAs might have in diagnosing and treating the disease. Furthermore, these data may provide a step toward feasible precision medicine for NOWS babies as well.</p>
</abstract>
<kwd-group>
<kwd>MicroRNAs</kwd>
<kwd>opioid use disorder (OUD)</kwd>
<kwd>neonatal opioid withdrawal syndrome (NOWS)</kwd>
<kwd>methylation</kwd>
<kwd>biomarkers</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Epigenomics and Epigenetics</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>Opioid use disorder (OUD) is a global health crisis that has led to a sharp increase in drug overdose deaths. Regular use of opioids during pregnancy can lead to Neonatal Opioid Withdrawal Syndrome (NOWS). This illness is associated with increased morbidity and mortality in infancy and is a significant risk factor with negative impacts on the neurodevelopment of infants. Every 15&#xa0;min a baby is born to a mother with an OUD (<xref ref-type="bibr" rid="B27">Honein et al., 2019</xref>), and 8.7 million children in the US have a parent with OUD (<xref ref-type="bibr" rid="B40">Lipari and Van Horn, 2013</xref>). Many <italic>in-utero</italic> opioid-exposed neonates are born prematurely below 32&#xa0;weeks of gestational age (<xref ref-type="bibr" rid="B15">Cleary et al., 2011</xref>) with a wide range of neurobiological symptoms. Acutely, neonates experiencing NOWS have autonomic nervous system dysfunction, insomnia, feeding difficulty, inconsolable crying, irritability, and seizures; they may require opioid medication and/or extended hospitalization. Longer-term sequalae of NOWS can include learning and cognitive disabilities (<xref ref-type="bibr" rid="B3">Baldacchino et al., 2015</xref>). The most common pharmacotherapies of choice for pregnant women with OUD include methadone or buprenorphine (<xref ref-type="bibr" rid="B49">Meyer et al., 2015</xref>). Previous genome-wide DNA methylation and transcriptome studies reported differentially methylated and expressed genes and multiple dysregulated biological pathways closely associated with mothers of infants with NOWS (<xref ref-type="bibr" rid="B59">Radhakrishna et al., 2021a</xref>; <xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>). Furthermore, gene-specific methylation variation profiles in mothers with OUD were reported in comparison to normal controls (<xref ref-type="bibr" rid="B13">Chorbov et al., 2011</xref>; <xref ref-type="bibr" rid="B72">Wachman et al., 2013</xref>; <xref ref-type="bibr" rid="B75">Wachman et al., 2018</xref>) and genetic variants were associated with NOWS risk and severity (<xref ref-type="bibr" rid="B55">Oei et al., 2012</xref>; <xref ref-type="bibr" rid="B72">Wachman et al., 2013</xref>; <xref ref-type="bibr" rid="B73">Wachman et al., 2015</xref>; <xref ref-type="bibr" rid="B44">Mactier et al., 2017</xref>; <xref ref-type="bibr" rid="B74">Wachman et al., 2017</xref>; <xref ref-type="bibr" rid="B48">Metpally et al., 2019</xref>).</p>
<p>Non-coding RNAs (ncRNAs) are emerging as key regulators of cellular processes such as genome integrity, cell growth, proliferation, differentiation, development, chromatin organization, gene expression, and signal transduction (<xref ref-type="bibr" rid="B57">Puvvula, 2019</xref>; <xref ref-type="bibr" rid="B39">Li et al., 2020</xref>). There are many classes of ncRNAs, with a few functionally important types including microRNA (miRNA), long non-coding RNA (lncRNA), circular RNA (circRNA), piwi-interfering RNA (piRNA), and small nucleolar RNA (snoRNAs). Dysregulation of these ncRNAs is involved in many cancers and also drug abuse (<xref ref-type="bibr" rid="B81">Zhang K. et al., 2016</xref>). MicroRNAs (miRNAs) are indeed one of the most prominent and extensively studied classes of non-coding RNAs (ncRNAs). They are small, single-stranded non-coding RNAs, and are highly conserved post-transcriptional negative regulators of gene expression by binding to the 3&#x2019; untranslated region of target mRNA (<xref ref-type="bibr" rid="B5">Bartel, 2004</xref>; <xref ref-type="bibr" rid="B83">Zhang et al., 2017</xref>). MicroRNAs have gained significant attention in the field of molecular biology and genetics due to their involvement in various biological processes, including gene regulation and disease pathogenesis (<xref ref-type="bibr" rid="B16">Contreras and Rao, 2012</xref>; <xref ref-type="bibr" rid="B4">Banerjee and Sen, 2015</xref>; <xref ref-type="bibr" rid="B29">Iacomino and Siani, 2017</xref>; <xref ref-type="bibr" rid="B67">Sliwinska et al., 2017</xref>; <xref ref-type="bibr" rid="B85">Zhou et al., 2018</xref>). Many diseases and conditions are influenced by variations in miRNA gene expression. Variations in miRNAs expression can result from deletions and duplications of larger sequences or chromosomes, mutations involving miRNA loci, or epigenetic methylation, among other factors (<xref ref-type="bibr" rid="B2">Ali Syeda et al., 2020</xref>).</p>
<p>Epigenetic mechanisms such as DNA methylation play an important role in regulating miRNA expression. Most of the miRNA genes were found in CpG-rich regions (<xref ref-type="bibr" rid="B7">Bianchi et al., 2017</xref>) and therefore DNA methylation may play an important role in altered miRNA expression. miRNAs can directly target epigenetic factors, such as DNA methyltransferases or histone deacetylases, thus regulating chromatin structure. These miRNAs are useful not only as diagnostic biomarkers for drug exposure but may also be neuroprotective in the context of drug use (<xref ref-type="bibr" rid="B63">Saugstad, 2010</xref>).</p>
<p>Since the 1993 discovery of miRNAs in <italic>Caenorhabditis elegans</italic> (<xref ref-type="bibr" rid="B37">Lee et al., 1993</xref>), there are now approximately 2500 defined human miRNAs, though many are without experimental validation (<xref ref-type="bibr" rid="B12">Chiang et al., 2010</xref>; <xref ref-type="bibr" rid="B35">Kozomara and Griffiths-Jones, 2014</xref>). The miRNAs do not require precise complementarity for target recognition (<xref ref-type="bibr" rid="B14">Christopher et al., 2016</xref>), a single miRNA can regulate the expression of multiple genes as its targets, while one gene may be targeted by many miRNAs (<xref ref-type="bibr" rid="B1">Adlakha and Saini, 2014</xref>). miRNA-based regulation is implicated in many disease etiologies and has been studied for treatment. Hence, miRNAs serve as an ideal unifying molecular marker to better understand the pathophysiological processes that may regulate gene expression. However, miRNAs association in placental tissues of mothers of <italic>in-utero</italic> opioid-exposed infants born with NOWS has not yet been fully addressed.</p>
<p>The placenta is a temporary fetal organ, which allows the exchange of nutrients and gases between the mother and the developing fetus. The placenta governs the development of fetal organs including the brain (<xref ref-type="bibr" rid="B9">Burton and Fowden, 2015</xref>). During pregnancy, miRNAs of placental origin are released continually in the maternal circulatory system, indicating that these miRNAs might serve as biomarkers for placental function during pregnancy and in cellular communication (<xref ref-type="bibr" rid="B51">Mouillet et al., 2015</xref>). Prior reports indicate that most addictive drugs easily cross the placenta and can affect fetal brain development (<xref ref-type="bibr" rid="B61">Ross et al., 2015</xref>).</p>
<p>As such, placenta-derived miRNAs may be assessed as surrogate markers for brain health at birth, following <italic>in-utero</italic> opioid exposure, and may predict the severity of NOWS before the emergence of physiological signs of withdrawal. However, the potential roles of placental miRNAs in OUD-pregnancy and NOWS outcomes are unknown. This study was undertaken to identify methylation differences in miRNA-encoding genes in the placentas of mothers with OUD who gave birth to infants with NOWS, which may help to identify potential biomarkers of NOWS.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>Materials and methods</title>
<p>This study was approved by the Institutional Ethics Committee of William Beaumont Health System, Royal Oak, MI, USA (2019&#x2013;086) and informed consent was waived as the research utilized paraffin blocks with archived records. The study was carried out under the principles of the Declaration of Helsinki. The study participants included only European Americans in the United States and details on the study cohort have been previously published (<xref ref-type="bibr" rid="B59">Radhakrishna et al., 2021a</xref>; <xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>). Inclusion criteria were the diagnosis of infants with NOWS born to opioid-misusing mothers. Mothers were evaluated for OUD using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, or DSM-5, assessment criteria (<xref ref-type="bibr" rid="B24">Hasin et al., 2013</xref>) by physicians/psychiatrists. Newborns with NOWS were studied by neonatologists based on ICD-10 clinical criteria (ICD-10 P96.1). All infants born to mothers with OUD were monitored in the inpatient unit for 4&#x2013;5&#xa0;days to observe for signs of NOWS. The infant was scored using the Finnegan Neonatal Abstinence Scoring Tool (FNAST), which was used to determine the use of pharmacological management with morphine. This scoring was done by the <italic>postpartum</italic> nurses and/or NICU nurses.</p>
<p>To identify DNA methylation patterns of miRNA encoding genes and their effect on expression, we analyzed 96 Formalin-Fixed Paraffin-Embedded (FFPE) archived placental tissue specimens for which DNA methylation and RNA sequencing (RNA-seq) data was recently published by our NOWS consortium (<xref ref-type="bibr" rid="B59">Radhakrishna et al., 2021a</xref>; <xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>). Placental specimens were obtained 2&#xa0;cm away from the umbilical cord insertion site on the mother&#x2019;s side of the placenta and were divided into 3 groups. Group 1, consisted of 32 infants prenatally exposed to opioids who received pharmacologic treatment for NOWS symptoms (&#x2b;Opioids/&#x2b;NOWS), and 32 infants, prenatally exposed to opioids that did not require pharmacologic therapy for NOWS (&#x2b;Opioids/-NOWS) and 32 unexposed controls. Among 64 opioid-exposed mothers, 61 (95%) had a history of tobacco smoking. Among 32 mothers (unexposed controls) consisted of individuals who did not have any opioid use and did not have infants diagnosed with NOW (-Opioids/-NOWS, control), 11 (34%) were smokers.</p>
<sec id="s2-1">
<title>DNA extraction, bisulfite conversion, and restoration</title>
<p>DNA was extracted from the FFPE placental blocks using a QIAamp DNA FFPE tissue kit (Qiagen, catalog no. 56404) according to the manufacturer&#x2019;s protocol. Infinium HD FFPE Restore kit (Illumina, San Diego, CA) was used for DNA restoration (<xref ref-type="bibr" rid="B66">Siegel et al., 2014</xref>) followed by bisulfite conversion of 500&#xa0;ng of DNA using the EZ DNA Methylation kit (Zymo Research, Irvine, CA) according to the manufacturer&#x2019;s instructions. The methodology has been detailed earlier (<xref ref-type="bibr" rid="B58">Radhakrishna et al., 2016</xref>; <xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>).</p>
</sec>
<sec id="s2-2">
<title>Illumina Infinium Methylation EPIC BeadChip</title>
<p>Genome-wide DNA methylation profiling was performed using the Infinium Methylation EPIC BeadChip array (Illumina Inc., San Diego, CA) with bisulfite-treated genomic DNA according to the manufacturer&#x2019;s protocol (<xref ref-type="bibr" rid="B71">Vishweswaraiah et al., 2019</xref>; <xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>). The Methylation EPIC BeadChip microarray provides quantitative measurement of 853,307 CpG sites, including the methylation of 9,961 CpG site regulators of miRNA-encoding genes present in the array (<xref ref-type="bibr" rid="B50">Moran et al., 2016</xref>). The sample placement in each chip was randomized to avoid confounding and to achieve successful microarray experiments (<xref ref-type="bibr" rid="B70">Verdugo et al., 2009</xref>). During the preprocessing of the methylation data, CpG-sites annotated to X and Y chromosomes and/or containing SNPs near or within the probe sequence (within 10&#xa0;bp of the CpG site), probes that lacked beta values, and probes with minor allele frequency exceeding 0.05 were excluded from the analysis (<xref ref-type="bibr" rid="B10">Chen et al., 2013</xref>; <xref ref-type="bibr" rid="B41">Liu et al., 2013</xref>; <xref ref-type="bibr" rid="B78">Wilhelm-Benartzi et al., 2013</xref>; <xref ref-type="bibr" rid="B19">Daca-Roszak et al., 2015</xref>), followed by the analysis of the beta values for the remaining CpG sites. SNPs within 10&#xa0;bp of the microRNA binding site may affect microRNA and mRNA interactions and reduce binding affinity (<xref ref-type="bibr" rid="B38">Li et al., 2016</xref>).</p>
</sec>
<sec id="s2-3">
<title>Network analysis of miRNAs</title>
<p>To establish associations between miRNAs and the biological processes they regulate in OUD/NOWs, we performed a gene ontology (GO) analysis. Pathway analysis was carried out using Ingenuity Pathway Analysis (IPA<sup>&#xae;</sup>) using differentially expressed miRNAs at FDR <italic>p</italic>-value &#x3c;0.05. MiRNAs were removed from the analysis if they were duplicated or unrecognized by IPA. The most statistically significant miRNAs identified in the NOWS were used in canonical pathways.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<p>The present cohort comprised 96 infants including 64 diagnosed with NOWS born to mothers with OUD and 32 unexposed controls (<xref ref-type="table" rid="T1">Table 1</xref>). We initially generated genome-wide methylation and gene expression measurements in the cohort mentioned above and published them (<xref ref-type="bibr" rid="B59">Radhakrishna et al., 2021a</xref>; <xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>). In the follow-up study, we aimed to identify miRNA genes in the proximity of CpG sites, in which modifications of the epigenetic profile are associated with NOWS. We analyzed the methylation of 9,961 CpG site regulators of miRNA-encoding genes present in the Illumina epic array.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Demographic characteristics of the study subjects: opioid-exposed NOWS newborns that need treatment (&#x2b;Opioids/&#x2b;NOWS), mothers with prenatally opioid-exposed infants that did not require treatment for NOWS (&#x2b;Opioids/-NOWS), and unexposed mothers with normal controls (-Opioids/-NOWS).</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left"/>
<th align="left"/>
<th align="center">Number of subjects</th>
<th align="center">Maternal age in years- Mean (SD)</th>
<th align="center">Gestational age at delivery in weeks&#x2013; Mean (SD)</th>
<th align="center">Alcohol history</th>
<th align="center">Tobacco history</th>
<th align="center">Birth weight (g) &#x2013;Mean (SD)</th>
<th align="center">NICU admission</th>
<th align="center">NOWS development</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="3" align="center">(&#x2b;Opioids/&#x2b;NOWS) versus (&#x2b;Opioids/-NOWS)</td>
<td align="center">Cases</td>
<td align="center">32</td>
<td align="center">31 (4.7)</td>
<td align="center">37.94 (3.16)</td>
<td align="center">4 (12%)</td>
<td align="center">30 (93%)</td>
<td align="center">2800 (780.7)</td>
<td align="center">32 (100%)</td>
<td align="center">32 (100%)</td>
</tr>
<tr>
<td align="center">Controls</td>
<td align="center">32</td>
<td align="center">28 (4.9)</td>
<td align="center">37.49 (2.96)</td>
<td align="center">3 (9%)</td>
<td align="center">31 (96%)</td>
<td align="center">2752 (671.2)</td>
<td align="center">11 (34%)</td>
<td align="center">0</td>
</tr>
<tr>
<td align="center">
<italic>q</italic>-value (FDR)</td>
<td align="center">NA</td>
<td align="center">0.9947</td>
<td align="center">0.9947</td>
<td align="center">0.9947</td>
<td align="center">0.9947</td>
<td align="center">0.9947</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">&#x3c;0.00001</td>
</tr>
<tr>
<td rowspan="3" align="center">&#x2b;Opioids/&#x2b;NOWS plus &#x2b;Opioids/-NOWS versus -Opioids/-NOWS</td>
<td align="center">Cases</td>
<td align="center">64</td>
<td align="center">29.5 (5.1)</td>
<td align="center">37.72 (3.07)</td>
<td align="center">7 (11%)</td>
<td align="center">61 (95%)</td>
<td align="center">2776 (729.7)</td>
<td align="center">43 (67%)</td>
<td align="center">32 (50%)</td>
</tr>
<tr>
<td align="center">Controls</td>
<td align="center">32</td>
<td align="center">32.5 (4.1)</td>
<td align="center">38.09 (3.37)</td>
<td align="center">3 (9%)</td>
<td align="center">11 (34%)</td>
<td align="center">3117 (760.6)</td>
<td align="center">3 (9%)</td>
<td align="center">0</td>
</tr>
<tr>
<td align="center">
<italic>q</italic>-value (FDR)</td>
<td align="center">NA</td>
<td align="center">0.8514</td>
<td align="center">0.0053</td>
<td align="center">0.0237</td>
<td align="center">0.0643</td>
<td align="center">0.0168</td>
<td align="center">0.0052</td>
<td align="center">0.0948</td>
</tr>
<tr>
<td rowspan="3" align="center">&#x2b;Opioids/&#x2b;NOWS versus -Opioids/-NOWS</td>
<td align="center">Cases</td>
<td align="center">32</td>
<td align="center">31 (4.7)</td>
<td align="center">37.94 (3.16)</td>
<td align="center">4 (12%)</td>
<td align="center">30 (93%)</td>
<td align="center">2800 (780.7)</td>
<td align="center">32 (100%)</td>
<td align="center">32 (100%)</td>
</tr>
<tr>
<td align="center">Controls</td>
<td align="center">32</td>
<td align="center">32.5 (4.02)</td>
<td align="center">38.09 (3.37)</td>
<td align="center">3 (9%)</td>
<td align="center">11 (34%)</td>
<td align="center">3117 (760.6)</td>
<td align="center">3 (9%)</td>
<td align="center">0</td>
</tr>
<tr>
<td align="center">
<italic>q</italic>-value (FDR)</td>
<td align="center">NA</td>
<td align="center">0.5747</td>
<td align="center">0.821</td>
<td align="center">0.2522</td>
<td align="center">0.024</td>
<td align="center">0.1243</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">&#x3c;0.00001</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>We identified 47 significantly differentially methylated (FDR <italic>p</italic>-value &#x2264;0.05) CpGs associated with 46 unique miRNAs, with a receiver operating characteristic (ROC) area under the curve (AUC) &#x2265;0.75 which were used in the follow-up study. Detailed hypomethylated and hypermethylated miRNAs were identified in the placentas of infants with NOWS (<xref ref-type="table" rid="T2">Tables 2</xref>; <xref ref-type="table" rid="T3">Table 3</xref>; <xref ref-type="table" rid="T4">Table 4</xref>). <xref ref-type="table" rid="T2">Table 2</xref> describes the miRNA dataset from &#x2b;Opioids/&#x2b;NOWS, which was compared against &#x2b; Opioids/-NOWS. miRNAs such as miR-301, miR-573, and miR-548 among others were found significantly differentially methylated. <xref ref-type="table" rid="T3">Table 3</xref> describe data distinguishing the combined two OUD groups from unexposed controls (&#x2b;Opioids/&#x2b;NOWS plus &#x2b; Opioids/-NOWS <italic>versus</italic> -Opioids/-NOWS, control)<bold>.</bold> The differentially methylated miRNAs identified include miR-181, miR-34, miR-129, and miR-548. Finally, <xref ref-type="table" rid="T4">Table 4</xref> defines &#x2b; Opioids/&#x2b;NOWS <italic>versus</italic> unexposed controls (-opioids/-NOWS), yielded the following differentially methylated miRNAs: miR-34, miR-10, miR-548, miR-518, miR-1909, and miR-1178.</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>The analysis of &#x2b;Opioids/&#x2b;NOWS <italic>versus</italic> &#x2b; Opioids/-NOWS. Significantly differentially methylated microRNAs based on FDR adjusted-p values &#x3c; 0.05 provided with (AUC) &#x2265;0.75 for NOWS detection. Details of corresponding CpG loci, chromosomes, and methylation status.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">TargetID</th>
<th rowspan="2" align="center">miR</th>
<th rowspan="2" align="center">CHR</th>
<th rowspan="2" align="center">
<italic>p</italic>-Val</th>
<th rowspan="2" align="center">FDR <italic>p</italic>-Val</th>
<th colspan="3" align="center">% Methylation</th>
<th colspan="3" align="center">CI</th>
<th rowspan="2" align="center">Methylation</th>
</tr>
<tr>
<th align="center">Cases</th>
<th align="center">Control</th>
<th align="center">Difference</th>
<th align="center">AUC</th>
<th align="center">lower</th>
<th align="center">upper</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">cg11210410</td>
<td align="center">miR-1268A</td>
<td align="center">9</td>
<td align="center">3.12539E-38</td>
<td align="center">2.70346E-32</td>
<td align="center">74.62</td>
<td align="center">64.79</td>
<td align="center">9.83</td>
<td align="center">0.82</td>
<td align="center">0.72</td>
<td align="center">0.93</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg14929554</td>
<td align="center">miR-5095</td>
<td align="center">16</td>
<td align="center">2.56839E-14</td>
<td align="center">2.22166E-08</td>
<td align="center">60.20</td>
<td align="center">70.37</td>
<td align="center">&#x2212;10.17</td>
<td align="center">0.76</td>
<td align="center">0.65</td>
<td align="center">0.88</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg16769912</td>
<td align="center">miR-558</td>
<td align="center">2</td>
<td align="center">1.85302E-13</td>
<td align="center">1.60286E-07</td>
<td align="center">60.52</td>
<td align="center">70.36</td>
<td align="center">&#x2212;9.83</td>
<td align="center">0.78</td>
<td align="center">0.67</td>
<td align="center">0.90</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg13790797</td>
<td align="center">miR-548G</td>
<td align="center">3</td>
<td align="center">4.61626E-11</td>
<td align="center">3.99307E-05</td>
<td align="center">49.01</td>
<td align="center">58.67</td>
<td align="center">&#x2212;9.66</td>
<td align="center">0.76</td>
<td align="center">0.64</td>
<td align="center">0.88</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg04312413</td>
<td align="center">miR-548F1</td>
<td align="center">1</td>
<td align="center">6.42158E-11</td>
<td align="center">5.55466E-05</td>
<td align="center">71.41</td>
<td align="center">78.91</td>
<td align="center">&#x2212;7.50</td>
<td align="center">0.77</td>
<td align="center">0.66</td>
<td align="center">0.89</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg02656609</td>
<td align="center">miR-301A</td>
<td align="center">17</td>
<td align="center">1.18711E-10</td>
<td align="center">0.000102685</td>
<td align="center">76.05</td>
<td align="center">82.73</td>
<td align="center">&#x2212;6.68</td>
<td align="center">0.81</td>
<td align="center">0.70</td>
<td align="center">0.92</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg10046367</td>
<td align="center">miR-548W</td>
<td align="center">17</td>
<td align="center">1.30176E-10</td>
<td align="center">0.000112602</td>
<td align="center">69.25</td>
<td align="center">76.95</td>
<td align="center">&#x2212;7.70</td>
<td align="center">0.83</td>
<td align="center">0.73</td>
<td align="center">0.93</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg26481500</td>
<td align="center">miR-1278</td>
<td align="center">1</td>
<td align="center">1.48897E-10</td>
<td align="center">0.000128796</td>
<td align="center">77.09</td>
<td align="center">83.57</td>
<td align="center">&#x2212;6.48</td>
<td align="center">0.78</td>
<td align="center">0.67</td>
<td align="center">0.89</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg23632539</td>
<td align="center">miR-5096</td>
<td align="center">1</td>
<td align="center">6.57986E-10</td>
<td align="center">0.000569158</td>
<td align="center">78.51</td>
<td align="center">71.87</td>
<td align="center">6.64</td>
<td align="center">0.74</td>
<td align="center">0.61</td>
<td align="center">0.86</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg15350946</td>
<td align="center">miR-1273H</td>
<td align="center">4</td>
<td align="center">6.59135E-10</td>
<td align="center">0.000570152</td>
<td align="center">45.23</td>
<td align="center">54.40</td>
<td align="center">&#x2212;9.18</td>
<td align="center">0.73</td>
<td align="center">0.61</td>
<td align="center">0.85</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg06769231</td>
<td align="center">miR-2117</td>
<td align="center">17</td>
<td align="center">6.72269E-10</td>
<td align="center">0.000581513</td>
<td align="center">64.17</td>
<td align="center">72.20</td>
<td align="center">&#x2212;8.03</td>
<td align="center">0.76</td>
<td align="center">0.64</td>
<td align="center">0.87</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg22395021</td>
<td align="center">miR-548H3</td>
<td align="center">6</td>
<td align="center">7.95169E-10</td>
<td align="center">0.000687821</td>
<td align="center">61.28</td>
<td align="center">69.57</td>
<td align="center">&#x2212;8.29</td>
<td align="center">0.70</td>
<td align="center">0.57</td>
<td align="center">0.83</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg00071872</td>
<td align="center">miR-548AE2</td>
<td align="center">16</td>
<td align="center">9.20749E-10</td>
<td align="center">0.000796448</td>
<td align="center">14.56</td>
<td align="center">9.27</td>
<td align="center">5.29</td>
<td align="center">0.72</td>
<td align="center">0.60</td>
<td align="center">0.85</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg18363417</td>
<td align="center">miR-573</td>
<td align="center">4</td>
<td align="center">2.94005E-09</td>
<td align="center">0.00254314</td>
<td align="center">73.90</td>
<td align="center">80.45</td>
<td align="center">&#x2212;6.54</td>
<td align="center">0.77</td>
<td align="center">0.65</td>
<td align="center">0.88</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg21254731</td>
<td align="center">miR-939</td>
<td align="center">8</td>
<td align="center">3.15349E-09</td>
<td align="center">0.002727773</td>
<td align="center">81.93</td>
<td align="center">87.17</td>
<td align="center">&#x2212;5.24</td>
<td align="center">0.86</td>
<td align="center">0.77</td>
<td align="center">0.95</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg02790122</td>
<td align="center">miR-1296</td>
<td align="center">10</td>
<td align="center">4.68281E-09</td>
<td align="center">0.00405063</td>
<td align="center">77.11</td>
<td align="center">83.10</td>
<td align="center">&#x2212;5.99</td>
<td align="center">0.71</td>
<td align="center">0.58</td>
<td align="center">0.84</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg26042267</td>
<td align="center">miR-3663HG</td>
<td align="center">10</td>
<td align="center">5.19928E-09</td>
<td align="center">0.004497379</td>
<td align="center">35.81</td>
<td align="center">28.35</td>
<td align="center">7.47</td>
<td align="center">0.72</td>
<td align="center">0.59</td>
<td align="center">0.84</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg25249290</td>
<td align="center">miR-181A1HG</td>
<td align="center">1</td>
<td align="center">8.31433E-09</td>
<td align="center">0.007191897</td>
<td align="center">61.56</td>
<td align="center">69.35</td>
<td align="center">&#x2212;7.79</td>
<td align="center">0.73</td>
<td align="center">0.61</td>
<td align="center">0.86</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg19350059</td>
<td align="center">miR-548F5</td>
<td align="center">13</td>
<td align="center">8.80098E-09</td>
<td align="center">0.007612844</td>
<td align="center">18.63</td>
<td align="center">13.02</td>
<td align="center">5.61</td>
<td align="center">0.74</td>
<td align="center">0.62</td>
<td align="center">0.87</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg00224335</td>
<td align="center">miR-4327</td>
<td align="center">21</td>
<td align="center">1.13961E-08</td>
<td align="center">0.009857667</td>
<td align="center">52.54</td>
<td align="center">44.68</td>
<td align="center">7.86</td>
<td align="center">0.75</td>
<td align="center">0.63</td>
<td align="center">0.87</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg17777998</td>
<td align="center">miR-1183</td>
<td align="center">7</td>
<td align="center">1.58092E-08</td>
<td align="center">0.013674961</td>
<td align="center">38.38</td>
<td align="center">46.76</td>
<td align="center">&#x2212;8.38</td>
<td align="center">0.71</td>
<td align="center">0.59</td>
<td align="center">0.84</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg13641189</td>
<td align="center">miR-658</td>
<td align="center">22</td>
<td align="center">2.75754E-08</td>
<td align="center">0.02385271</td>
<td align="center">11.27</td>
<td align="center">6.90</td>
<td align="center">4.37</td>
<td align="center">0.79</td>
<td align="center">0.68</td>
<td align="center">0.91</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg03425468</td>
<td align="center">miR-92B</td>
<td align="center">1</td>
<td align="center">3.25876E-08</td>
<td align="center">0.028188258</td>
<td align="center">11.44</td>
<td align="center">7.06</td>
<td align="center">4.38</td>
<td align="center">0.75</td>
<td align="center">0.63</td>
<td align="center">0.87</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg24289256</td>
<td align="center">miR-548F3</td>
<td align="center">1</td>
<td align="center">4.69893E-08</td>
<td align="center">0.040645767</td>
<td align="center">72.95</td>
<td align="center">79.16</td>
<td align="center">&#x2212;6.21</td>
<td align="center">0.70</td>
<td align="center">0.58</td>
<td align="center">0.83</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg03135982</td>
<td align="center">miR-3650</td>
<td align="center">5</td>
<td align="center">4.94006E-08</td>
<td align="center">0.042731525</td>
<td align="center">76.26</td>
<td align="center">82.01</td>
<td align="center">&#x2212;5.75</td>
<td align="center">0.74</td>
<td align="center">0.62</td>
<td align="center">0.87</td>
<td align="center">Hypo</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Analysis of (&#x2b;Opioids/&#x2b;NOWS), &#x2b; (&#x2b;Opioids/-NOWS), <italic>versus</italic> (-Opioids/-NOWS, control). Differentially methylated microRNAs and the corresponding methylated CpG sites with Target ID, Gene ID, chromosome location, <italic>p</italic>-value, FDR <italic>p</italic>-value, and % methylation change are given.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Target ID</th>
<th rowspan="2" align="center">miR</th>
<th rowspan="2" align="center">CHR</th>
<th rowspan="2" align="center">
<italic>p</italic>-Val</th>
<th rowspan="2" align="center">FDR <italic>p</italic>-Val</th>
<th colspan="3" align="center">% Methylation</th>
<th rowspan="2" align="center">AUC</th>
<th colspan="3" align="center">CI</th>
</tr>
<tr>
<th align="center">Cases</th>
<th align="center">Control</th>
<th align="center">Difference</th>
<th align="center">lower</th>
<th align="center">upper</th>
<th align="center">Methylation</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">cg11479035</td>
<td align="center">miR-1276</td>
<td align="center">15</td>
<td align="center">3.00299E-37</td>
<td align="center">2.59759E-31</td>
<td align="center">86.38</td>
<td align="center">79.64</td>
<td align="center">6.74</td>
<td align="center">0.83</td>
<td align="center">0.73</td>
<td align="center">0.93</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg13767940</td>
<td align="center">miR-34B</td>
<td align="center">11</td>
<td align="center">2.67438E-17</td>
<td align="center">2.31334E-11</td>
<td align="center">9.13</td>
<td align="center">16.99</td>
<td align="center">&#x2212;7.86</td>
<td align="center">0.72</td>
<td align="center">0.59</td>
<td align="center">0.85</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg23632539</td>
<td align="center">miR-5096</td>
<td align="center">1</td>
<td align="center">2.20382E-13</td>
<td align="center">1.9063E-07</td>
<td align="center">80.63</td>
<td align="center">73.11</td>
<td align="center">7.51</td>
<td align="center">0.82</td>
<td align="center">0.72</td>
<td align="center">0.92</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg19782652</td>
<td align="center">miR-125A</td>
<td align="center">19</td>
<td align="center">1.19367E-09</td>
<td align="center">0.001032525</td>
<td align="center">78.09</td>
<td align="center">84.18</td>
<td align="center">&#x2212;6.09</td>
<td align="center">0.92</td>
<td align="center">0.85</td>
<td align="center">0.99</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg19069367</td>
<td align="center">miR-6850</td>
<td align="center">8</td>
<td align="center">1.61143E-09</td>
<td align="center">0.001393891</td>
<td align="center">9.86</td>
<td align="center">5.38</td>
<td align="center">4.48</td>
<td align="center">0.82</td>
<td align="center">0.72</td>
<td align="center">0.92</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg21913981</td>
<td align="center">miR-1178</td>
<td align="center">12</td>
<td align="center">2.14827E-09</td>
<td align="center">0.001858257</td>
<td align="center">73.86</td>
<td align="center">66.75</td>
<td align="center">7.10</td>
<td align="center">0.79</td>
<td align="center">0.68</td>
<td align="center">0.90</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg19756622</td>
<td align="center">miR-518A1; miR-518E</td>
<td align="center">19</td>
<td align="center">7.21604E-09</td>
<td align="center">0.006241877</td>
<td align="center">81.17</td>
<td align="center">75.24</td>
<td align="center">5.93</td>
<td align="center">0.84</td>
<td align="center">0.74</td>
<td align="center">0.94</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg24774002</td>
<td align="center">miR-1909</td>
<td align="center">19</td>
<td align="center">3.6236E-08</td>
<td align="center">0.031344152</td>
<td align="center">89.10</td>
<td align="center">92.79</td>
<td align="center">&#x2212;3.69</td>
<td align="center">0.83</td>
<td align="center">0.73</td>
<td align="center">0.93</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg13477253</td>
<td align="center">miR-548G</td>
<td align="center">3</td>
<td align="center">5.62137E-08</td>
<td align="center">0.048624868</td>
<td align="center">15.48</td>
<td align="center">10.50</td>
<td align="center">4.98</td>
<td align="center">0.77</td>
<td align="center">0.66</td>
<td align="center">0.89</td>
<td align="center">Hyper</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Analysis of (&#x2b;Opioids/&#x2b;NOWS), <italic>versus</italic> (-Opioids/-NOWS, control). Details of CpG targets significantly differentially methylated microRNAs in NOWS. Differentially methylated CpG sites with Target ID, Gene ID, chromosome location, <italic>p</italic>-value, FDR <italic>p</italic>-value, and % methylation change details are given.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">CpG target</th>
<th rowspan="2" align="center">miR</th>
<th rowspan="2" align="center">CHR</th>
<th rowspan="2" align="center">
<italic>p</italic>-value</th>
<th rowspan="2" align="center">FDR <italic>p</italic>-Val</th>
<th colspan="3" align="center">% Methylation</th>
<th rowspan="2" align="center">AUC</th>
<th colspan="3" align="center">CI</th>
</tr>
<tr>
<th align="center">Cases</th>
<th align="center">Control</th>
<th align="center">diffreence</th>
<th align="center">lower</th>
<th align="center">upper</th>
<th align="center">Methylation</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">cg20276377</td>
<td align="left">miR-548G</td>
<td align="center">3</td>
<td align="center">1.98166E-23</td>
<td align="center">1.71414E-17</td>
<td align="center">15.95839</td>
<td align="center">25.9444</td>
<td align="center">&#x2212;9.98601</td>
<td align="center">0.65</td>
<td align="center">0.53</td>
<td align="center">0.77</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg01514668</td>
<td align="left">miR-129-2</td>
<td align="center">11</td>
<td align="center">8.5878E-13</td>
<td align="center">7.42844E-07</td>
<td align="center">12.95384</td>
<td align="center">19.57678</td>
<td align="center">&#x2212;6.62294</td>
<td align="center">0.58</td>
<td align="center">0.46</td>
<td align="center">0.70</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg18576861</td>
<td align="left">miR-3621</td>
<td align="center">9</td>
<td align="center">4.85176E-11</td>
<td align="center">4.19677E-05</td>
<td align="center">23.36382</td>
<td align="center">30.90311</td>
<td align="center">&#x2212;7.53929</td>
<td align="center">0.74</td>
<td align="center">0.63</td>
<td align="center">0.86</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg20351875</td>
<td align="left">miR-548C</td>
<td align="center">12</td>
<td align="center">1.0169E-10</td>
<td align="center">8.79621E-05</td>
<td align="center">16.31689</td>
<td align="center">22.83412</td>
<td align="center">&#x2212;6.51723</td>
<td align="center">0.67</td>
<td align="center">0.55</td>
<td align="center">0.79</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg25147193</td>
<td align="left">miR-181C</td>
<td align="center">19</td>
<td align="center">1.44145E-10</td>
<td align="center">0.000124685</td>
<td align="center">8.817696</td>
<td align="center">13.97549</td>
<td align="center">&#x2212;5.157794</td>
<td align="center">0.64</td>
<td align="center">0.52</td>
<td align="center">0.76</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg11479035</td>
<td align="left">miR-1276</td>
<td align="center">15</td>
<td align="center">2.1566E-10</td>
<td align="center">0.000186546</td>
<td align="center">84.82172</td>
<td align="center">79.64312</td>
<td align="center">5.1786</td>
<td align="center">0.77</td>
<td align="center">0.66</td>
<td align="center">0.87</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg06871184</td>
<td align="left">miR-1238</td>
<td align="center">19</td>
<td align="center">4.3656E-10</td>
<td align="center">0.000377624</td>
<td align="center">64.27575</td>
<td align="center">57.02732</td>
<td align="center">7.24843</td>
<td align="center">0.72</td>
<td align="center">0.60</td>
<td align="center">0.83</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg13781167</td>
<td align="left">miR-8081</td>
<td align="center">9</td>
<td align="center">1.58717E-09</td>
<td align="center">0.001372905</td>
<td align="center">64.99973</td>
<td align="center">71.75161</td>
<td align="center">&#x2212;6.75188</td>
<td align="center">0.70</td>
<td align="center">0.58</td>
<td align="center">0.81</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg19756622</td>
<td align="left">miR-518A1</td>
<td align="center">19</td>
<td align="center">3.09143E-09</td>
<td align="center">0.002674085</td>
<td align="center">80.68514</td>
<td align="center">75.23586</td>
<td align="center">5.44928</td>
<td align="center">0.82</td>
<td align="center">0.72</td>
<td align="center">0.92</td>
<td align="center">Hyper</td>
</tr>
<tr>
<td align="center">cg17540499</td>
<td align="left">miR-592</td>
<td align="center">7</td>
<td align="center">3.8092E-09</td>
<td align="center">0.00329496</td>
<td align="center">31.95645</td>
<td align="center">39.38867</td>
<td align="center">&#x2212;7.43222</td>
<td align="center">0.59</td>
<td align="center">0.47</td>
<td align="center">0.71</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg13767940</td>
<td align="left">miR-34B</td>
<td align="center">11</td>
<td align="center">1.06906E-08</td>
<td align="center">0.009247326</td>
<td align="center">11.87235</td>
<td align="center">16.98866</td>
<td align="center">&#x2212;5.11631</td>
<td align="center">0.64</td>
<td align="center">0.52</td>
<td align="center">0.76</td>
<td align="center">Hypo</td>
</tr>
<tr>
<td align="center">cg26438516</td>
<td align="left">miR-1256</td>
<td align="center">10</td>
<td align="center">2.79234E-08</td>
<td align="center">0.024153783</td>
<td align="center">66.7636</td>
<td align="center">60.35334</td>
<td align="center">6.41026</td>
<td align="center">0.71</td>
<td align="center">0.60</td>
<td align="center">0.83</td>
<td align="center">Hyper</td>
</tr>
</tbody>
</table>
</table-wrap>
<sec id="s3-1">
<title>Pathway analysis of significant miRNAs</title>
<p>Gene ontology analysis found major dysregulated pathways affected by methylation changes on miRNAs associated with NOWS and OUD-related outcomes. A comparison was made between three groups i. Distinguishing NOWS from prenatal opioid-exposure without NOWS (&#x2b;Opioids/&#x2b;NOWS, <italic>versus</italic> &#x2b; Opioids/-NOWS), ii. Distinguishing prenatal opioid use <italic>versus</italic> unexposed controls (OUD detection) (&#x2b;Opioids/&#x2b;NOWS and &#x2b;Opioids/-NOWS <italic>versus</italic> -Opioids/-) and iii. distinguishing NOWS <italic>versus</italic> unexposed controls (&#x2b;Opioids/&#x2b;NOWS <italic>versus</italic> &#x2b; Opioids/-NOWS). Significant functional biological processes regulated by these miRNAs were identified with the plausible molecular mechanism of differentially expressed miRNAs. Network interaction between miRNAs and their target genes was provided in <xref ref-type="fig" rid="F1">Figure 1</xref>, <xref ref-type="fig" rid="F2">Figure 2</xref>, and <xref ref-type="fig" rid="F3">Figure 3</xref>.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Ingenuity pathway analysis (IPA) of significant methylation regulators of miRNA-encoding genes and network analysis with a <italic>p</italic>-value &#x3c;0.05 are depicted using &#x2b; Opioids/&#x2b;NOWS <italic>versus</italic> &#x2b; Opioids/-NOWS.</p>
</caption>
<graphic xlink:href="fgene-14-1215472-g001.tif"/>
</fig>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Pathway analysis of &#x2b;Opioids/&#x2b;NOWS and &#x2b;Opioids/-NOWS <italic>versus</italic> -Opioids/-.</p>
</caption>
<graphic xlink:href="fgene-14-1215472-g002.tif"/>
</fig>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Pathway analysis of &#x2b;Opioids/&#x2b;NOWS <italic>versus</italic> -Opioids/-NOWS.</p>
</caption>
<graphic xlink:href="fgene-14-1215472-g003.tif"/>
</fig>
</sec>
<sec id="s3-2">
<title>Target identification of differentially methylated miRNAs</title>
<p>MicroRNA Target Prediction and Functional Study Database (miRDB) server (<ext-link ext-link-type="uri" xlink:href="http://miRdb.org">http://miRdb.org</ext-link>) were used to predict putative targets of aberrantly methylated miRNAs. With a set target score of &#x2265;60 (<xref ref-type="bibr" rid="B77">Wang, 2016</xref>), we searched the miRDB database for predictions of targets of some of these significant miRNAs. From the miRDB, we retrieved the target genes for 47 miRNAs. From this, we discovered multiple predicted targets for each miRNA (<xref ref-type="bibr" rid="B1">Adlakha and Saini, 2014</xref>). These predicted 47 miRNA targets were compared with the differentially methylated genes identified in our genome-wide methylation data of the same sample cohort (<xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>). The results showed that multiple predicted targets (genes) of these miRNAs were also found to be differentially methylated in our patients. <xref ref-type="sec" rid="s10">Supplementary Tables S1 to S15</xref> provide examples of miRNA targets discussed in the manuscript that are important and available in the miRDB database.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<sec id="s4-1">
<title>Description of opioid use with NOWS vs. opioid use without NOWS</title>
<p>(opioid-exposed infants who required pharmacologic management for NOWS <italic>versus</italic> opioid-exposed infants that did not require treatment for NOWS) (&#x2b;Opioids/&#x2b;NOWS <italic>versus</italic> &#x2b; Opioids/-NOWS) (<xref ref-type="table" rid="T2">Table 2</xref>). The <italic>miR-130</italic> family includes four members (<italic>miR-130a, miR-130b, miR-301a, and miR-301b</italic>) with the same source sequences and which perform similar biological functions (<xref ref-type="bibr" rid="B76">Wang et al., 2020</xref>). The <italic>miR-130</italic> mature miRNA is known to increase the expression of <italic>PPARA</italic> mRNA (<xref ref-type="bibr" rid="B56">Papi et al., 2013</xref>). <italic>PPARA</italic> is a nuclear hormone receptor that responds to certain types of cannabinoids (<xref ref-type="bibr" rid="B54">O&#x27;Sullivan, 2016</xref>).</p>
<p>
<italic>MiR-130</italic> is regulated by <italic>TP53, HNF4A, AGO2, TNFRFS1B,</italic> and insulin, and was found to regulate the expressions of <italic>KDR, IL4, IFNG, TNF,</italic> and <italic>RELA</italic>. This regulation is likely dampened to a certain extent due to methylation, resulting in higher protein expressions and activations of the miRNA. <italic>MiR-130</italic> regulates <italic>IL4</italic>, which in turn regulates the mu-opioid receptor (MOR), whose principal ligands include opioid peptides and analgesics. The MOR also has an important role in dependence on other drugs of abuse, such as nicotine, cocaine, and alcohol via its modulation of the dopamine system.</p>
<p>Interestingly, <italic>miR-130</italic> putatively targets several other genes that are preferentially involved in brain disorders. For instance, <italic>miR-130</italic>, in combination with other miRNAs, jointly regulates <italic>ATXN1</italic>, which causes spinocerebellar ataxia type 1 (SCA1). SCA1 causes seizures, slurred speech, slowness of movement, and cognitive impairments. Similar neurological signs such as slurred speech or slowed movements are seen in OUD subjects, and conditions such as tremors and seizures are common in both OUD and infants with NOWS. Our genome-wide methylation analysis of NOWS revealed hypermethylation of the <italic>ATXN1</italic> gene (<xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>).</p>
<p>Inhibition of <italic>miR-130</italic> leads to decreased translocation of phosphorylated RELA protein to nuclei (<xref ref-type="bibr" rid="B25">Hazra et al., 2019</xref>). <italic>RELA</italic> protein (<xref ref-type="bibr" rid="B11">Chen et al., 2006</xref>) plays a significant role in the downstream activation of the <italic>OPRD1</italic> (also known as <italic>DOR</italic>, &#x3b4;-opioid receptor, or delta-opioid receptor) is a paralog of <italic>OPRM1</italic> associated with opioid dependence gene that encodes the delta-opioid receptor (<italic>DOR</italic>) protein in humans. In the central nervous system, DOR and mu-opioid receptors (<italic>MOR</italic>) can interact and modulate each other&#x2019;s and are involved in the regulation of pain (<xref ref-type="bibr" rid="B6">Beaudry et al., 2015</xref>; <xref ref-type="bibr" rid="B52">Olesen et al., 2018</xref>). <italic>DOR</italic> is responsible for reducing the intensity of pain signals, while <italic>MOR</italic> is responsible for reducing the frequency of pain signals. Heterodimerization of <italic>MOR</italic> and <italic>DOR</italic> can indeed impact the recruitment of beta-arrestin 2 (<italic>ARRB2</italic>) protein, which subsequently influences downstream intracellular signaling (<xref ref-type="bibr" rid="B62">Rozenfeld and Devi, 2007</xref>).</p>
</sec>
<sec id="s4-2">
<title>Description of opioid exposure vs. unexposed controls</title>
<p>(opioid-exposed infants who required pharmacologic management for NOWS &#x2b; opioid-exposed infants that did not require treatment for NOWS <italic>versus</italic> unexposed controls. (&#x2b;Opioids/&#x2b;NOWS plus &#x2b; Opioids/-NOWS <italic>versus</italic> -Opioids/-NOWS). <xref ref-type="table" rid="T3">Table 3</xref> describes the miRNA dataset from mothers with a history of opioid usage. This cohort was compared against normal unexposed controls and showed significant methylation differences in miR-34<bold>,</bold> miR-129, miR-181, and miR-548 among others.</p>
<p>The binding of the <italic>AGO2</italic> protein was found to occur with <italic>miR-34</italic> mature miRNAs (<xref ref-type="bibr" rid="B79">Wu et al., 2011</xref>). The presence of <italic>AGT</italic> proteins was found to increase expressions of mouse <italic>miR-34</italic> and <italic>miR-129</italic> mature miRNAs indicating positive regulation by <italic>AGT</italic> (<xref ref-type="bibr" rid="B31">Jin et al., 2012</xref>; <xref ref-type="bibr" rid="B28">Huo et al., 2019</xref>). <italic>AGT</italic> protein increases paracrine activation of <italic>CNR1</italic> protein (<xref ref-type="bibr" rid="B69">Turu et al., 2009</xref>), followed by clustering of <italic>CNR1</italic> with <italic>CNR2</italic>. Activated <italic>CNR2</italic> protein increases the expression of <italic>OPRM1</italic> (<xref ref-type="bibr" rid="B8">Borner et al., 2006</xref>) possibly resulting in an <italic>OPRM1-OPRD1-OPRL1-OPRK1</italic> feedback loop.</p>
<p>The binding of the <italic>TP53</italic> response element from the <italic>miR-34</italic> promoter and <italic>TP53</italic> protein was found to occur followed by targeting of <italic>TP53</italic> mRNA by <italic>miR-34</italic> mature miRNA. <italic>TP53</italic> protein further increases the transcription of the <italic>miR-34</italic> gene and <italic>miR-34</italic> mature miRNA was found to, in turn, upregulate the expression of acetylated (K382) <italic>p53</italic> protein followed by increasing activity of <italic>p53</italic> protein (<xref ref-type="bibr" rid="B20">Feng et al., 2011</xref>). Protein modification of <italic>AGO2</italic> leads to an increased expression of <italic>miR-548</italic> and <italic>miR-181</italic> (<xref ref-type="bibr" rid="B31">Jin et al., 2012</xref>; <xref ref-type="bibr" rid="B21">Garibaldi et al., 2016</xref>). Homozygous experimental <italic>p53</italic> gene deletion was found to decrease the expression of <italic>miR-548</italic> mature miRNA (<xref ref-type="bibr" rid="B65">Shin et al., 2009</xref>). Experimental interference of human <italic>p53</italic> mRNA by shRNA led to an increased expression of <italic>miR-519</italic> mature miRNA (<xref ref-type="bibr" rid="B21">Garibaldi et al., 2016</xref>). Binding of 3&#x2032;UTR from <italic>ZEB2</italic> mRNA and <italic>miR-181A</italic> mature miRNA occurs, indicating probable regulation of <italic>ZEB2</italic> mRNA. Negative regulation of <italic>TP53</italic> in this pathway leads to activations of several genes in the pathway including <italic>PPARA</italic> which is a nuclear hormone receptor capable of taking certain cannabinoid ligands.</p>
<p>Women who used illicit or unprescribed opioids during pregnancy have a higher risk of fetal growth restriction and preterm delivery (<xref ref-type="bibr" rid="B45">Maghsoudlou et al., 2017</xref>). Recent studies have shown differential <italic>miR-548</italic> expression profile was associated with spontaneous preterm births (<xref ref-type="bibr" rid="B22">Gray et al., 2017</xref>). Placental DNA methylation of <italic>miR-548</italic> and <italic>WWTR1</italic> genes influence insulin sensitivity during pregnancy, and preterm birth and is linked to insulin resistance (<xref ref-type="bibr" rid="B26">Hofman et al., 2004</xref>; <xref ref-type="bibr" rid="B46">Mathai et al., 2012</xref>). Interestingly, nine of the 47 dysregulated miRNAs belonged to the <italic>miR-548</italic> family, including <italic>miR548G, miR548F1, miR548W, miR548H3, miR548AE2, miR548F5, miR548F3, miR548C</italic>, and <italic>miR548C</italic>, all of which were hypermethylated. Moreover, <italic>WWTR1</italic> is also hypermethylated in the present study.</p>
</sec>
<sec id="s4-3">
<title>NOWS vs. unexposed controls</title>
<p>(opioid-exposed infants who required pharmacologic management for NOWS <italic>versus</italic> unexposed controls (&#x2b;Opioids/&#x2b;NOWS <italic>versus</italic> -opioids/-NOWS, control) (<xref ref-type="table" rid="T4">Table 4</xref>). MiR-515 is the largest miRNA gene cluster in humans (<xref ref-type="bibr" rid="B82">Zhang M. et al., 2016</xref>), the family members of <italic>miR-515</italic> include <italic>miR-516</italic> and <italic>miR-518</italic> (<xref ref-type="bibr" rid="B82">Zhang M. et al., 2016</xref>). <italic>MiRNA-515</italic> and <italic>miR-34</italic> are found to be connected directly to opioid and cannabinoid receptors respectively. <italic>MiR-515</italic> was found to regulate opioid genes <italic>OPRM1</italic>, and <italic>OPRD1</italic>, while miR-34 was found to regulate cannabinoid genes <italic>GPR19</italic> and <italic>PPARA</italic> via Notch and <italic>TP53</italic> proteins.</p>
<p>Mature miR-515 is known to decrease the activity of the human nuclear factor kappa B (NFkB) complex (<xref ref-type="bibr" rid="B33">Keklikoglou et al., 2012</xref>), however, in the absence of active <italic>miR-515</italic> due to methylation as seen in the current cohort, the Nfkb complex is actively expressed leading to continued expression of <italic>OPRM1</italic> mRNA. Thus, methylation of <italic>miR-515</italic> results in the transcription of human <italic>OPRM1</italic> mRNA which otherwise would be under the strict regulation of the <italic>NFkB</italic> complex (<xref ref-type="bibr" rid="B36">Kraus et al., 2003</xref>; <xref ref-type="bibr" rid="B47">Memet, 2006</xref>).</p>
<p>
<italic>RELA</italic> mRNA is targeted by <italic>miR-515</italic> mature miRNA leading to its decreased expression (<xref ref-type="bibr" rid="B33">Keklikoglou et al., 2012</xref>). Interference of <italic>Rela</italic> mRNA by siRNA decreases the expression of <italic>Oprd1</italic> mRNA. <italic>RELA</italic> protein increases activation of the reporter gene with a promoter fragment (-262&#x2013;1) from the <italic>OPRD1</italic> gene (<xref ref-type="bibr" rid="B11">Chen et al., 2006</xref>). Methylation of miR-515 leads to the absence of regulation leading to an increased expression of <italic>RELA</italic>. Increased <italic>RELA</italic> subsequently increases its downstream partner <italic>OPRD1</italic>. The <italic>OPRM1</italic> and <italic>OPRD1</italic> genes were also found to be distinctively methylated in the previously described datasets. <italic>OPRM1</italic> was found hypomethylated in mothers who used opioids during pregnancy and delivered NOWS newborns which required immediate medical care when compared to mothers who had used opioids during pregnancy and delivered newborns without NOWS. In contrast, <italic>OPRD1</italic> was found hypermethylated in mothers with a history of opioid usage who delivered babies without NOWS when compared against unexposed controls. Both <italic>OPRD1</italic> and <italic>OPRM1</italic> were found to be distinctly methylated in the current dataset of mothers who used opioids and delivered NOWS babies when compared against unexposed controls. However, <italic>PPARA</italic> activation status differed across these three datasets.</p>
</sec>
<sec id="s4-4">
<title>Role of <italic>PPARA</italic> in conjunction with opioid and cannabinoid receptors</title>
<p>The absence of active <italic>miR-130</italic> in the &#x2b;Opioid/&#x2b;NOWS group may have led to decreased expression of <italic>PPARA</italic> mRNA, therefore, leading to reduced production of oxidation enzymes to effectively scavenge for free radicals generated by oxidative stress from opioid use. This may have resulted in mothers delivering newborns with NOWS that required medical intervention. Interestingly, when the Opioid&#x2b;/NOWS- group was compared against unexposed controls, <italic>PPARA</italic> was found to be under the positive regulation of <italic>TP53</italic> leading to its activation. This protective role of <italic>PPARA</italic> was absent in the &#x2b;Opioids/&#x2b;NOWS cohort of mothers when compared against unexposed controls. This is possibly due to the active status of <italic>miR-134</italic> and/or to a different set of regulatory partners Notch and <italic>TP53</italic> whose role in this context is not known and can only be speculated. MiR-34 was found to regulate cannabinoid genes including <italic>PPARA</italic> via Notch and <italic>TP53</italic> proteins. Though the anti-oxidation activity of <italic>PPARA</italic> was probably negated by the extensive and consistent activations of opioid genes <italic>OPRM1,</italic> and <italic>OPRD1</italic>, unlike seen in any of the other two cohorts and thus resulting in mothers delivering NOWS newborns that required medical care. Moreover, <italic>miR-34</italic> has a functional role in the regulation of stress-induced anxiety, depression with suicidal ideation (<xref ref-type="bibr" rid="B23">Haramati et al., 2011</xref>). Anxiety and depression are common and normal phenomena, in mothers of infants experiencing NOWS (<xref ref-type="bibr" rid="B17">Corr et al., 2020</xref>).</p>
<p>
<italic>SIRT1</italic> which is hypomethylated in the current cohort study (FDR) <italic>p</italic> &#x2264; 0.05) (<xref ref-type="bibr" rid="B60">Radhakrishna et al., 2021b</xref>), is a nicotinamide adenosine dinucleotide (NAD)-dependent deacetylase that removes acetyl groups from various proteins. <italic>SIRT1</italic> normally functions to limit the expression of <italic>miR-134</italic>, which targets the critical activity-dependent transcription factor (CREB) essential for learning and memory (<xref ref-type="bibr" rid="B43">Lv et al., 2013</xref>; <xref ref-type="bibr" rid="B53">O&#x27;Neal-Moffitt et al., 2014</xref>; <xref ref-type="bibr" rid="B42">Liu et al., 2017</xref>). <italic>SIRT1</italic> exerts neuroprotection against ischemic injury and various neurodegenerative disorders (<xref ref-type="bibr" rid="B80">Xu et al., 2018</xref>). Dysregulation of brain <italic>SIRT1</italic> activity can have devastating brain consequences including neurological dysfunction (<xref ref-type="bibr" rid="B80">Xu et al., 2018</xref>).</p>
<p>Experimental interference of human <italic>p53</italic> mRNA by shRNA led to an increased expression of <italic>miR-519</italic> mature miRNA (<xref ref-type="bibr" rid="B21">Garibaldi et al., 2016</xref>). Homozygous experimental <italic>p53</italic> gene deletion was found to decrease the expression of <italic>miR-548</italic> mature miRNA (<xref ref-type="bibr" rid="B65">Shin et al., 2009</xref>). <italic>MiR-10</italic> which belongs to the <italic>miR-125A</italic> family member (<xref ref-type="bibr" rid="B68">Tehler et al., 2011</xref>) was found to decrease the activation of <italic>p53</italic> (<xref ref-type="bibr" rid="B32">Joo et al., 2013</xref>). Studies on mouse mmu-miR-10 mature miRNA were found to decrease the expression of mouse <italic>p53</italic> protein (<xref ref-type="bibr" rid="B64">Sen et al., 2014</xref>). Targeting of <italic>LIN28</italic> mRNA by <italic>miR-10</italic> and <italic>miR-34</italic> mature miRNAs was found to occur leading to decreased translation of <italic>LIN28</italic> (<xref ref-type="bibr" rid="B84">Zhong et al., 2010</xref>; <xref ref-type="bibr" rid="B30">Jain et al., 2012</xref>). <italic>p53</italic> (<italic>TP53</italic>) protein was found to decrease the binding of <italic>BRF1</italic> protein and RNA polymerase iii complexes (<xref ref-type="bibr" rid="B18">Crighton et al., 2003</xref>). Experimental inhibition of active RNA polymerase iii complexes was found to result in increased expression of <italic>miR-1909</italic> while decreasing the expression of <italic>miR-1178</italic> mature miRNA (<xref ref-type="bibr" rid="B34">Koo et al., 2015</xref>) demonstrating the role of transcriptional regulation by RNA polymerase iii.</p>
</sec>
<sec id="s4-5">
<title>Limitations</title>
<p>The study has some limitations, namely, a lack of racial or ethnic diversity, given that it focused on individuals of European origin. A second limitation is that we have not replicated our findings in an independent study cohort, and therefore, we will continue to carry out <italic>in vitro</italic> and <italic>in vivo</italic> experiments to validate the conclusions in the future.</p>
</sec>
<sec id="s4-6">
<title>Summary and future directions</title>
<p>To our knowledge, this is the first report about miRNA methylation levels of NOWS on a previously well-documented large cohort of placental tissue specimens with genome-wide methylation profiles and gene expression profiles. NOWS is associated with an altered miRNA methylation pattern in the placenta, suggesting that miRNA deregulation is involved in the pathogenesis of NOWS. The current experimental data suggest that epigenetic variations in the placental tissue can serve as surrogate markers for brain health at birth, and thus infant micro-RNA &#x201c;signatures&#x201d; can predict the severity of NOWS even before withdrawal symptoms begin. The differential methylation of several miRNAs has been found in association with NOWS. The findings have biological plausibility, and they include several known biological pathways and genes with possible mechanisms associated with NOWS development. The identified dysregulated pathways and genes may therefore provide important opportunities for the development of novel future miRNA-based therapeutic approaches for NOWS. Several preclinical and clinical trials have been reported for miRNA-based therapeutics and few reports indicate that miRNA expression levels can be modified by modulating the miRNA processing pathway. However, validation with large NOWS cohorts and using robust techniques for DNA methylation analysis of miRNA genes are warranted before the application in clinical practice.</p>
</sec>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s5">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s10">Supplementary Material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s6">
<title>Author contributions</title>
<p>Conceptualization UR and RB-S; investigation, UR, RB-S, SN, WB, and RM, resources, UR, RB-S, and SN; funding acquisition, UR, SN, and RB-S; data curation, UR, SN, LU, AF, SM, RC, WB, RM, AV, SV, and RB-S; writing&#x2014;original draft preparation, UR and AV; and writing&#x2014;Review and editing, UR, SN, LU, AF, SM, RC, WB, RM, AV, SV, and RB-S; All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s7">
<title>Funding</title>
<p>The work was supported by both anonymous donors and Beaumont Health System, Royal Oak, MI.</p>
</sec>
<sec sec-type="COI-statement" id="s8">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s9">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s10">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fgene.2023.1215472/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fgene.2023.1215472/full&#x23;supplementary-material</ext-link>
</p>
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</sec>
<sec id="s11">
<title>Abbreviations</title>
<p>Hypo, Hypomethylation; Hyper; Hypermethylation.</p>
</sec>
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