Prenatal opioid exposure significantly impacts placental protein kinase C (PKC) and drug transporters, leading to drug resistance and neonatal opioid withdrawal syndrome

Abstract

Background: Neonatal Opioid Withdrawal Syndrome (NOWS) is a consequence of in-utero exposure to prenatal maternal opioids, resulting in the manifestation of symptoms like irritability, feeding problems, tremors, and withdrawal signs. Opioid use disorder (OUD) during pregnancy can profoundly impact both mother and fetus, disrupting fetal brain neurotransmission and potentially leading to long-term neurological, behavioral, and vision issues, and increased infant mortality. Drug resistance complicates OUD and NOWS treatment, with protein kinase regulation of drug transporters not fully understood.

Methods: DNA methylation levels of ATP-binding cassette (ABC) and solute carrier (SLC) drug transporters, along with protein kinase C (PKC) genes, were assessed in 96 placental samples using the Illumina Infinium MethylationEPIC array (850K). Samples were collected from three distinct groups: 32 mothers with infants prenatally exposed to opioids who needed pharmacological intervention for NOWS, 32 mothers with prenatally opioid-exposed infants who did not necessitate NOWS treatment, and 32 mothers who were not exposed to opioids during pregnancy.

Results: We identified 69 significantly differentially methylated SLCs, with 24 hypermethylated and 34 hypomethylated, and 11 exhibiting both types of methylation changes including SLC13A3, SLC15A2, SLC16A11, SLC16A3, SLC19A2, and SLC26A1. We identified methylation changes in 11 ABC drug transporters (ABCA1, ABCA12, ABCA2, ABCB10, ABCB5, ABCC12, ABCC2, ABCC9, ABCE1, ABCC7, ABCB3): 3 showed hypermethylation, 3 hypomethylation, and 5 exhibited both. Additionally, 7 PKC family genes (PRKCQ, PRKAA1, PRKCA, PRKCB, PRKCH, PRKCI, and PRKCZ) showed methylation changes. These genes are associated with 13 pathways involved in NOWS, including ABC transporters, bile secretion, pancreatic secretion, insulin resistance, glutamatergic synapse, and gastric acid secretion.

Conclusion: We report epigenetic changes in PKC-related regulation of drug transporters, which could improve our understanding of clinical outcomes like drug resistance, pharmacokinetics, drug-drug interactions, and drug toxicity, leading to maternal relapse and severe NOWS. Novel drugs targeting PKC pathways and transporters may improve treatment outcomes for OUD in pregnancy and NOWS.

Keywords: bile secretion; biomarker; drug transporters; insulin resistance; neonatal opioid withdrawal syndrome SLC transporters ABC transporters; opioid use; pancreatic secretion; protein kinases C.

FIGURE 1

Receiver operating characteristic (ROC) curve analysis of methylated CpGs in ABC, SLC, and PKC genes (FDR-p ≤ 0.05) that had good diagnostic accuracy with CpGs AUC ≥ 0.80-0.89). AUC: Area Under the Receiver Operating Characteristics Curve; 95% CI: 95% Confidence Interval. Confidence intervals (CI) in parentheses show bounds.

FIGURE 2

Protein-Protein Interaction Network from STRING Analysis for Differentially Methylated Genes. Interactions are depicted as lines connecting nodes, which represent individual proteins. Line colors correspond to the type of evidence supporting the interaction: red for gene fusion; green for the neighborhood; blue for co-occurrence; purple for experimental; yellow for text mining; light blue for database evidence; and black for co-expression.

FIGURE 3

Gene Ontology (GO) Analysis for Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The bar chart displays the enrichment scores (−log10 (p.adjust) for the top ten GO terms within each category. Elevated enrichment scores indicate a higher level of significance, suggesting that these GO terms are notably overrepresented in the dataset, and may be integral to the underlying mechanisms of the biological system being investigated.

FIGURE 4

KEGG Pathway Analysis. This bubble plot visualizes the enriched pathways among the differently methylated genes based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. Pathways are listed on the y-axis and are ordered by the enrichment score (−log10(p.adjust)), plotted on the x-axis. The size of each bubble corresponds to the gene count within the pathway, and the color gradient represents the adjusted p-value, with darker hues indicating higher significance.

FIGURE 5

Heatmap displaying normalized beta values representing the top differentially methylated CpG sites in subjects exposed to prenatal opioids, along with CpG sites associated with transporters and protein kinase C (PKCs). Beta values for CpG sites with an unadjusted p < 0.005 and |Δβ| ≥ 0.05 are shown. Each row contains individual samples (affected and controls), and the columns pertain to the beta value for a CpG site color-coded from 0-1 (see color key, top right). The analysis was done in four combinations. Hierarchical sorting was performed by the CpG-associated genes site (columns), and the dendrogram (left) indicates similarities in methylation trends across all CpG sites. (A) Analysis I was conducted to distinguish NOWS from prenatal opioid exposure without NOWS symptoms. Heatmap comparing +Opioids/+NOWS and +Opioids/-NOWS reveals significant differences in methylation status among SLC and ABC drug transporter genes, alongside PKCs family members linked to NOWS. (B) Analysis II was conducted to distinguish prenatal opioid abuse from normal controls (OUD detection). Heatmap analyzing (+Opioids/+NOWS), (+Opioids/-NOWS), versus (-Opioids/-NOWS, control). (C) Analysis III was conducted to distinguish NOWS from unexposed controls. Heatmap displaying a comparison of (+Opioids/+NOWS) versus (-Opioids/-NOWS, control). (D) Analysis IV was conducted to distinguish opioid-induced epigenetic changes. Heatmap displaying analysis of (+Opioids/-NOWS), versus (-Opioids/-NOWS, control).