A Pilot Multiplex Salivary Transcriptomic Analysis to Understand the Sex-specific Effects of Maternal Opioid Use in Offspring

A Pilot Multiplex Salivary Transcriptomic Analysis to Understand the Sex-specific Effects of Maternal Opioid Use in Offspring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Pilot Multiplex Salivary Transcriptomic Analysis to Understand the Sex-specific Effects of Maternal Opioid Use in Offspring Tomoko Kaneko-Tarui, Francesca Carasi-Schwartz, Kiran Singh, Kelsea R. Gildawie, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7418166/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Opioid use disorder affects males and females differently, yet the molecular mechanisms are understudied in neonates. Our laboratory has demonstrated differential sex effects of opioids on the reward and inflammatory pathways related to neonatal feeding behavior that may affect growth and cardiometabolic outcomes. This observational pilot study examined the sex-specific impact of maternal opioid use during pregnancy on reward, energy homeostasis, inflammation, oxidative stress, and neuropathology pathways in offspring. Saliva from nine opioid-exposed and nine non-exposed neonates collected within 48 hours after birth underwent a multiplex, high-throughput analysis of 72 select genes using NanoString’s nCounter® system (NanoString Technologies, Seattle, WA, USA. Despite low RNA abundance in neonatal saliva, experimental conditions were optimized after several trials. Multiplex analysis demonstrated sex-specific molecular effects of maternal opioid use, i.e., upregulated pathways related to reward, inflammation, oxidative stress, feeding, and energy homeostasis pathways in males, and downregulated pathways related to inflammation and cardiovascular function in females. This pilot study demonstrates the feasibility of multiplexing neonatal saliva using a high-throughput platform. Future work will replicate these methods and validate findings in a larger sample to clarify the sex-specific short and long-term impact of maternal opioid use on health outcomes. Health sciences/Diseases Health sciences/Medical research Biological sciences/Neuroscience Biological sciences/Physiology maternal offspring opioid exposure sex differences saliva multiplex transcriptomics Introduction The incidence of opioid use disorder (OUD) among pregnant individuals has steadily increased 1,2 , accompanied by a parallel surge in neonatal opioid withdrawal syndrome (NOWS). NOWS affects approximately 6–20 per 1,000 live births, depending on the geographic region and population 3–5 . Medications for opioid use disorder (MOUD), including methadone and buprenorphine, are the standard of care for pregnant individuals with OUD 6,7 . These opioid agonist therapies reduce illicit opioid use, improve maternal health, and promote prenatal care engagement. However, both drugs cross the placenta and can influence fetal neurodevelopment, potentially affecting neonatal feeding behavior, immune function, and long-term health outcomes 8–10 . In addition to the clinical management of acute withdrawal in the period after birth, prenatal opioid exposure is also associated with more chronic and long-term effects, including low birth weight, small-for-gestational-age (SGA) status, impaired postnatal growth, and neurodevelopmental delays 9,11–13 . Therefore, maternal OUD is a significant public health concern, with profound implications for neonatal health and developmental outcomes. Despite the well-documented clinical manifestations and risks, the molecular mechanisms underlying neonatal responses to opioid exposure in utero remain understudied. Emerging evidence from both human and animal studies suggests that sex-specific biological responses may play a critical role in shaping the trajectory of these outcomes. For instance, male neonates exposed to opioids in utero are more vulnerable to severe NOWS and exhibit poorer cognitive and behavioral outcomes compared to opioid-exposed females 14,15 . These sex differences are likely driven by variations in immune, metabolic, and neurodevelopmental pathways 16 . Recent studies have begun to elucidate molecular pathways that may mediate these sex-specific effects of opioid exposure. One such pathway involves the dopamine receptor D2 (DRD2), a critical component of the brain’s reward signaling system 17,18 , which has been implicated in opioid-related reinforcement behaviors. In addition, animal models indicate that opioids can activate toll-like receptor 4 (TLR4) on microglia, triggering proinflammatory cascades that enhance opioid reward and may contribute to neuroinflammation 19,20 . Our laboratory was the first to demonstrate sex-specific effects of maternal opioid use on DRD2 expression, suggesting a potential mechanism for aberrant feeding behavior in neonates, particularly males 21 . We also demonstrated that upregulation of proinflammatory genes - interleukin-6 ( IL6 ), interleukin-1 beta ( IL1β ), and tumor necrosis factor alpha ( TNFα ) - might underlie white matter injury in opioid-exposed offspring, with greater effects observed in females than in males 22 . Together, these findings highlight that reward signaling and immune activation may be differentially regulated by opioids depending on neonatal sex. Building on these data, the current pilot study aimed to identify additional candidate genes that may address the critical knowledge gap of understanding the sex-specific effects of maternal opioid use. Leveraging multiplex, high-throughput commercial techniques that have not been previously applied to neonatal saliva, we profiled gene pathways related to opioid use, including inflammation, reward, feeding regulation, oxidative stress, and energy metabolism 16–19,21–23 . In addition to opioid exposure and sex, we also explored whether molecular signatures vary by the types of maternal opioid use (methadone, buprenorphine). Results Participant Characteristics Following an initial quality check (QC) against the standard for multiplex transcriptomics and further optimization of the transcriptomic experiments (see Methods), samples from 18 neonates (nine opioid-exposed, nine non-exposed) passed the QC and were included in the final analysis. As shown in Table 1 , opioid-exposed neonates were more likely to be SGA (33.3% vs. 0%) and born to mothers with hepatitis C (55.6% vs. 0%) compared to non-exposed neonates. Additionally, opioid-exposed neonates were smaller at birth (weight, length, and head circumference). There was a higher proportion of females in the non-exposed group (77.8%) relative to the opioid-exposed group (33.3%). Table 1 Demographic Data Non-Exposed (N = 9) Opioid-Exposed (N = 9) P Female 7 (77.8) 3 (33.3) 0.06 White 7 (77.8) 8 (88.9) 0.59 Non-Hispanic 6 (66.7) 8 (88.9) 0.12 GA (weeks) 37.0 (1.6) 37.6 (1.6) 0.46 BW (grams) 2955.1 (433.8) 2721.4 (624.9) 0.37 BW percentile 62.9 (49.0, 66.0) 28.0 (4.0, 46.0) 0.02 Length (cm) 48.3 (2.1) 46.8 (4.5) 0.38 Length percentile 55.0 (37.0, 73.0) 23.0 (10.0, 37.0) 0.07 HC (cm) 33.7 (0.9) 33.0 (2.8) 0.46 HC percentile 74.0 (60.0, 75.0) 21.0 (12.3, 49.0) 0.06 SGA 0 (0.0) 3 (33.3) < 0.01 Cesarean section 6 (66.7) 4 (44.4) 0.34 1-minute Apgar 8.0 (8.0, 9.0) 8.0 (8.0, 8.0) 0.84 5-minute Apgar 9.0 (9.0, 9.0) 9.0 (8.0, 9.0) 0.56 Maternal smoking 1 (11.1) 4 (44.4) 0.09 Maternal Hepatitis C 0 (0.0) 5 (55.6) < 0.01 Maternal GBS 0 (0.0) 2 (22.2) 0.32 Maternal medication Buprenorphine Methadone NA 7 (77.8) 2 (22.2) NA Polysubstance NA 6 (66.7) NA NA = not available/applicable, GA = gestational age, BW = birth weight, HC = head circumference, SGA = small for gestational age. Data are presented as mean (standard deviation) or median (interquartile ranges) for continuous measures, and N (%) for categorical measures. Bolded p values represent significant differences. Gene Expression Differences by Opioid Exposure Comparing opioid-exposed with non-exposed neonates, gene expression analyses revealed significant dysregulation in several inflammation- and neurodevelopment-related genes (Table 2 ). Notably, PTGS2 ( COX 2 ), a key mediator of inflammation, was significantly upregulated in the exposed group (fold change: 3.00, p = 0.02). In contrast, expression of CCL2 and BDNF was downregulated (fold changes: − 3.42 and − 2.38, respectively; p < 0.05), suggesting suppressed immune regulation and neuronal signaling. Table 2 Differential Gene Expression by Opioid Exposure By Exposure Genes Abbreviation Pathways/diseases* Fold Change P value All (9 exp. vs 9 non-exp.) CCL2 C-C motif chemokine ligand 2, monocyte chemoattractant protein-1 ( MCP-1 ) Immunoregulatory, inflammation, neurodegenerative disorder, cancer, cardiovascular diseases -3.42 0.03 BDNF Brain derived neurotrophic factor Neuronal development, cognition, memory, learning, neuropsychiatry -2.38 0.03 PTGS2 Prostaglandin-endoperoxide synthase 2, cyclooxygenase 2 ( COX 2 ) Inflammation, prostaglandin biosynthesis 3.00 0.02 Females (3 exp. vs 7 non-exp.) CCL2 C-C motif chemokine ligand 2, monocyte chemoattractant protein-1 ( MCP-1 ) Immunoregulatory, inflammation, neurodegenerative disorder, cancer, cardiovascular diseases -8.14 0.03 CXCR4 C-X-C motif chemokine receptor 4 Immune response, neurological functions, cancer progression -6.21 0.01 IL6 Interleukin 6 Inflammation, immune response, cancer progression, obesity, diabetes -5.29 0.05 Males (6 exp. vs 2 non-exp.) DRD2 Dopamine receptor 2 Reward sensitivity, locomotion, cognition, emotion regulation 6.04 0.05 RELA REL proto-oncogene, NF-ĸB subunit, p65 Oxidative stress, inflammation, autoimmune disorders, cancer 5.94 < 0.01 MC4R Melanocortin 4 receptor Energy homeostasis, body weight and feeding regulation, obesity 5.54 0.05 FOS Fos proto-oncogene Immune response, inflammation, cancer 4.96 0.04 PTGS2 Prostaglandin-endoperoxide synthase 2 Inflammation, prostaglandin biosynthesis 4.33 0.05 SQSTM1 Sequestosome 1 (p62) Immunity, neurodegenerative disorders, cancer 3.14 0.02 By Opioid Type Genes Abbreviation Pathways/diseases* Fold Change P value 2 Methadone vs. 7 Buprenorphine CXCR4 C-X-C motif chemokine receptor 4 Immune response, neurological functions, cancer progression 9.38 < 0.01 TNF Tumor necrosis factor Neuronal development, cognition, memory, learning, neuropsychiatry 5.40 0.04 SQSTM1 Sequestosome 1 (p62) Immunity, neurodegenerative disorders, cancer 3.14 0.02 * source: Rosalind® Academy version 3.39.13.1 ( https://rosalind.bio/academy ); exp: opioid-exposed Stratified analyses revealed marked sex-differential effects of maternal opioid use. Within the female cohort, maternal opioid use downregulated inflammation-related genes, including CCL2 , CXCR4 , and IL6 (fold changes: − 8.14 to − 5.29; p = 0.01–0.05). In contrast, in the male cohort, maternal opioid use upregulated DRD2 , RELA , MC4R, FOS, PTGS2 , and SQSTM1 (fold changes: 3.14 to 6.04; p < 0.01–0.05). These genes are implicated in reward signaling, oxidative stress response, feeding and energy regulation, as well as cognition and neurodegenerative disorders. Interestingly, cancer was also involved in several of these pathways. Differences by Types of Opioid Exposure Comparisons between neonates exposed to methadone (n = 2) versus buprenorphine (n = 7) showed that methadone was associated with significantly greater expression of immune-related genes. Specifically, CXCR4 and TNF were upregulated in the methadone group (fold changes: 9.38 and 5.40; p < 0.05), as was SQSTM1 , a gene involved in autophagy, immunity, and neurodegenerative disorders (fold change: 3.14, p = 0.02) (Table 2 ). A few of these pathways were associated with cancer. Sex-Specific Differences in Gene Expression Independent of exposure status, sex-stratified analysis across all neonates showed that males had lower expression of metabolic genes such as PPARα , AKT1 , and AMPK compared to females (fold changes: − 4.10 to − 2.46; all p < 0.05), and greater expression of prostaglandin biosynthesis factor PTGS2 , angiogenic and atherosclerotic factor VEGFα , and metabolic homeostasis LEPR (fold changes: 2.5 to 3.75, p < 0.05). Among non-exposed neonates, RELA was downregulated in males compared to females (fold change: − 4.30, p = 0.02), suggesting a lower baseline inflammatory state. However, in opioid-exposed neonates, males showed substantial upregulation of inflammatory and immune pathway genes, including PTGS2 , TLR4 , IL1β , CXCR4 , and CXCL8 (fold changes: 2.20 to 6.50; all p < 0.05), underscoring a sex-specific activation of immune and neuroinflammatory pathways in response to maternal opioid use. Cancer progression again presented and paralleled the inflammation in some of these pathways (Table 3 ). Table 3 Differential Gene Expression by Sex By Sex Genes Abbreviation Pathways/diseases* Fold Change P value All (8 males vs 10 females) PPARA Peroxisome proliferator activated receptor alpha Energy, cholesterol, and lipid metabolism, glucose homeostasis, immune, inflammation, obesity, atherosclerosis -4.10 0.01 AKT1 AKT serine/threonine kinase 1 Cell metabolism, insulin signaling, cancer, -3.24 0.01 AMPK AMP-activated protein kinase Energy metabolism, fatty acid oxidation, inflammation, diabetes, obesity, metabolic disorder -2.46 0.04 PTGS2 Prostaglandin-endoperoxide synthase 2 Inflammation, prostaglandin biosynthesis 3.75 < 0.01 VEGFA Vascular endothelial growth factor A Angiogenesis, atherosclerosis, hypoxic-induced neovascularization, obesity, tumor growth, cancer 3.15 0.03 LEPR Leptin receptor Energy homeostasis, metabolism, insulin signaling, glucose homeostasis, obesity, cancer, metabolic syndrome, cardiovascular disease 2.5 0.03 Non-exp. (2 males vs 7 females) RELA REL proto-oncogene, NFĸB subunit, p65 Oxidative stress, inflammation, autoimmune disorders, cancer -4.30 0.02 Exp. (6 males vs 3 females) PTGS2 Prostaglandin-endoperoxide synthase 2, cyclooxygenase 2 ( COX 2 ) Inflammation, prostaglandin biosynthesis 6.50 0.01 TLR4 Toll-like receptor 4 Innate immunity, pathogen recognition, inflammation, microglia activation, neuroinflammation, cancer, autoimmune disorders, neurodegeneration 5.88 0.05 IL1B Interleukin 1 beta Inflammation, neuroinflammation, cancer, diabetes, bipolar disorder, Alzheimer’s disease 5.49 0.04 CXCR4 C-X-C motif chemokine receptor 4 Immune response, neurological functions, cancer progression 2.29 0.03 CXCL8 C-X-C motif ligand 8 Inflammation, cancer, neurodegeneration, neuroinflammation, Parkinson’s disease 2.20 0.05 * source: Rosalind® Academy version 3.39.13.1 ( https://rosalind.bio/academy ); exp: opioid-exposed Discussion This pilot study provides novel evidence of sex-specific molecular responses in neonates prenatally exposed to maternal methadone and buprenorphine. To our knowledge, this study is the first to leverage a high-throughput, commercial multiomic profiling platform for neonatal saliva using the nCounter® Analysis System (NanoString Technologies, Seattle, WA, USA). By adapting this platform for saliva, we identified distinct patterns in inflammation, reward signaling, and energy regulation pathways, offering insight into the biological mechanisms underlying differential neonatal vulnerability. Our findings support and extend prior observations from animal models and emerging human data, while introducing neonatal saliva as a feasible biospecimen for molecular analysis. Miller et al. reported reduced neutrophils and inflammatory cytokines in the umbilical cord blood of opioid-exposed neonates compared to those in non-exposed neonates 24 . In contrast, Newville et al. observed elevated proinflammatory cytokines and chemokines, along with heightened immune reactivity, in methadone-exposed rats 25 . Our results integrate these findings by showing that maternal opioid use modulates inflammatory responses in offspring, with the direction and magnitude of changes differing by sex. Specifically, the downregulation of CCL2 and BDNF expression, alongside the upregulation of PTGS2 , indicates that the inflammatory responses following in utero opioid exposure represent a complex interplay of biological and environmental factors. For example, the female-predominant downregulation of CCL2 may underlie the overall reduced expression of this gene in the exposed cohort. In contrast, the increased expression of PTGS2 in exposed males likely drives the elevated levels observed. Thus, sex appears to mediate the inflammatory consequences of maternal opioid use, as evidenced by male-versus female-specific molecular alterations identified in this study. These alterations span pathways related to inflammation, immune regulation, neurodevelopment, cognition, metabolic homeostasis, and neurodegenerative risk. Therefore, our pilot study highlights the need to include sex as a biological variable in understanding the impact of maternal opioid use on offspring. The greater expression of DRD2 in opioid-exposed males in the current study aligns with our published data showing the male-predominant effects of opioids on heightened reward signaling and feeding behavior 19 . Of interest is the greater expression of MC4R in opioid-exposed males. Given that MC4R is critical for feeding and metabolism and is typically upregulated in a satiated state 26 , the concurrent increase in DRD2 and MC4R expression in opioid-exposed neonates suggests that maternal opioid use dysregulates the hypothalamic balance and may explain the feeding dysregulation often observed in these neonates 27 . The elevated expression of RELA and MC4R further implicates oxidative stress and disrupted energy homeostasis in male neonates, consistent with studies linking prenatal opioid exposure to metabolic dysregulation 27 and long-term neurobehavioral consequences 9,11–13 . While our data need to be validated in a larger sample size, these findings provide a critical foundation to link sex-specific molecular changes with clinical presentations in opioid-exposed neonates to arrive at effective interventions and personalized care. Stratification by sex (Table 3 ) provides further evidence of the distinct molecular response to in utero opioid exposure between male and female neonates. Opioid-exposed males exhibited upregulation of inflammation, neuroinflammation, and neurodegenerative disorders as shown by the greater expression of TLR4, PTGS2, IL1β, CXCR4 , and CXCL8 . While opioids act via opioid receptors, animal data have demonstrated the non-neuronal effects of opioids by binding with TLR4 in microglia and the release of chemokines and cytokines, and subsequent reinforcement of reward signaling 18 . Our study is the first to suggest that maternal opioid use may modulate inflammation through its sex-specific effect on the TLR4 pathway, which may explain the upregulation of DRD2 in opioid-exposed males. In contrast, opioid-exposed females showed marked downregulation of genes in the inflammation and immune pathways. Our findings align with previous evidence that female neonates may possess intrinsic immunological advantages and resilience to prenatal stressors 28 and that male sex confers a greater vulnerability to adverse prenatal and perinatal effects 29 . The directionality and magnitude of these differences point to a biologically plausible framework in which inflammation and reward circuits are differentially modulated by sex, potentially influencing the severity of NOWS and future neurodevelopment in the offspring. Another important observation, albeit limited by our small sample size, is the differential gene expression based on the type of maternal opioid use. Neonates exposed to methadone demonstrated upregulation of genes in the inflammatory and immune pathways compared to those exposed to buprenorphine. Based on studies showing worse clinical outcomes in neonates exposed to methadone relative to buprenorphine (e.g., more severe NOWS and more extended hospital stays) 30–32 , the molecular data presented here may suggest a biological basis for these clinical trends. This study is the first to use neonatal saliva on a high-throughput, commercial transcriptomic platform. Despite the initial assay challenges, our team successfully optimized the experimental conditions to generate the current data. The technical descriptions reported in this study serve as proof of concept that using micro quantities of neonatal saliva to conduct multiplexed gene analyses is feasible and even desirable. Neonatal research is often limited by the types of procedures that can be used to generate data; therefore, the field must leverage the least invasive method possible to ensure safe and robust research in this population. In addition to the technical novelty, our molecular findings provide intriguing findings that can be utilized in future studies examining the mechanistic and clinical relevance of maternal opioid use in offspring. First, our results reinforce the role of TLR4 in opioid-related neuroimmune interactions, consistent with animal data showing microglial activation and inflammatory priming following opioid exposure 19,33 . Second, the greater expression of DRD2 may underscore the higher OUD prevalence in adult males than in females. In other words, the heightened reward signaling early in life may predispose to future reward-seeking behaviors, such as substance use or other types of addiction, with differential sex effects. Third, the consistent enrichment of metabolism-related pathways among opioid-exposed neonates—especially males—needs to be further studied as these early metabolic alterations may contribute to the lower birth weight, aberrant feeding behavior, and growth impairment observed in this population, all of which could lead to cardiometabolic issues in adulthood. Fourth, while the significance of cancer involvement in the pathways affected by maternal opioid use is unclear, future longitudinal studies should examine the risk of malignancy related to in utero and childhood opioid exposure. This is especially important given the higher risk of cancer and cancer-related mortality in people with chronic opioid use 34–36 , although a meta-analysis cautioned against the risk of bias in overestimating the overall effect of opioid use on cancer outcomes 36 . Finally, using drops of neonatal saliva, our pilot study highlights the importance of including sex as a biological variable. The current data further support other studies from our group and others, demonstrating the sex-specific risks related to OUD and its impact on offspring. Understanding the dimorphic effects of sex can personalize the care of neonatal opioid withdrawal, which currently is based on a one-size-fits-all paradigm. From a translational perspective, these results highlight the potential for salivary biomarkers to identify neonates at higher risk of adverse outcomes. Such markers could guide personalized care strategies, including closer neurodevelopmental follow-up or early behavioral interventions. Limitations of the current study include the small sample size, which reduces statistical power and generalizability. The use of saliva, while non-invasive and clinically practical, captures a subset of systemic molecular activity and may not fully reflect brain-specific processes. Additionally, potential confounding by maternal comorbidities (e.g., hepatitis C, polysubstance use) and unmeasured environmental exposures cannot be excluded. Finally, the cross-sectional design precludes conclusions about long-term consequences, underscoring the need for longitudinal follow-up. Future research should expand sample sizes, include additional tissue types such as cord blood or placenta, neuroimaging data, and developmental screening tests to better understand the associations with neurodevelopmental outcomes, as well as comprehensive cardiometabolic evaluations, including blood pressure and anthropometric measurements. Integrating multi-omics approaches—including epigenetics and proteomics—may further elucidate sex-specific biological programming in opioid-exposed neonates. Importantly, future studies should evaluate whether salivary gene expression profiles can serve as reliable predictors of clinical outcomes or treatment response in neonates with NOWS. Our study also serves as a foundation for a collaboration with preclinical researchers using non-human models to examine the mechanistic underpinnings of maternal opioid use. In conclusion, maternal opioid use is associated with distinct, sex-specific molecular responses in human neonates, with opioid-exposed males showing increased activation of inflammatory and reward-related pathways, and females displaying relative downregulation of immune signaling. These findings suggest that biological sex plays a critical role in shaping neonatal vulnerability to in utero opioid exposure, potentially contributing to the variable severity of NOWS and long-term developmental outcomes. The observed upregulation of DRD2 and TLR4 in opioid-exposed males provides compelling evidence for a mechanistic link between immune activation and altered reward sensitivity, consistent with animal models of opioid reinforcement. Moreover, differential expression patterns based on the type of opioid—methadone versus buprenorphine—highlight the importance of individualized approaches to maternal treatment. Saliva-based gene expression profiling offers a promising, non-invasive tool to uncover early biomarkers of risk, which may inform sex- and exposure-specific clinical monitoring and intervention strategies. Larger, longitudinal studies are needed to validate these findings and explore their relevance to neurodevelopmental trajectories, ultimately advancing personalized care for opioid-exposed neonates. Methods Study Design and Participants This pilot observational study used a prospective cohort design to investigate differences in gene expression between male and female neonates, comparing those with prenatal opioid exposure to those without. A total of 18 neonates were enrolled from the newborn nursery and the neonatal intensive care unit of Tufts Medical Center in Boston, Massachusetts, between February and November 2023. The study cohort included nine neonates with documented maternal opioid use during pregnancy (opioid-exposed group) and nine neonates without such exposure (non-exposed group). Neonates were matched by gestational age to the extent possible. Inclusion criteria for the opioid-exposed group included: (1) maternal opioid use confirmed by medical record documentation or toxicology screening, and (2) availability of saliva samples collected within 48 hours of birth. The non-exposed group consisted of neonates born to mothers with no known substance use during pregnancy and no detectable opioids in toxicology screening. Exclusion criteria included: major congenital anomalies, perinatal infection, or gestational age < 34 weeks. Maternal demographic data (including race, ethnicity, delivery type, opioid type, group B streptococcus/GBS colonization status, hepatitis C status, cigarette smoking status) and neonatal clinical characteristics (gestational age, sex, birth weight, length, head circumference and corresponding percentiles, Apgar 1 and 5 minutes, SGA status) were obtained from the medical record. Ethical Approval and Consent to Participate The study was approved by the Tufts Medical Center Institutional Review Board. All procedures were performed in accordance with institutional and national research committee ethical standards, the Declaration of Helsinki, and relevant guidelines and regulations. Written informed consent for study participation was obtained from a parent or legal guardian of each neonate prior to enrollment. No identifiable images or personal data are included in this manuscript, and all HIPAA identifiers have been removed. Sample Collection and RNA Extraction Saliva samples were collected from neonates within 48 hours of birth using our established techniques 37 . Briefly, saliva was collected using a 1-milliliter (mL) insulin syringe (Becton, Dickinson and Company, Franklin Lakes, NJ) attached to the low-pressure wall suction for 15–30 seconds. To minimize breast milk and associated maternal RNA contamination, saliva was collected before feeding or at least 30 minutes after feeding. Saliva was immediately placed in 250 microliter (µL) RNAprotect Saliva Reagent (Qiagen, Hilden, Germany) to minimize RNA degradation. Total RNA was isolated using the RNeasy Micro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocols, which were optimized for low-input, saliva-derived specimens. On-column DNase treatment was performed using RNase-free DNase I (Qiagen) to minimize DNA contamination. Once RNA was extracted, the total RNA was stored at -80⁰C pending gene expression analysis. Gene Expression Analysis mRNA expression profiling was performed using the NanoString nCounter Analysis System (NanoString Technologies, Seattle, WA), a direct, multiplexed platform for quantifying gene expression. A custom CodeSet was designed to target 72 genes of interest, including selected housekeeping genes used for normalization. To prepare samples for hybridization, the NanoString Low RNA Input Kit (NanoString Technologies, Seattle, WA) was used according to the manufacturer's instructions. We subsequently determined that eight pre-amplification cycles were optimal for amplifying the mRNA transcripts using neonatal saliva, which is known to have a low starting mRNA level 38 . The nCounter assay employs a molecular barcoding system, in which specific probes hybridize directly to target mRNAs. Each probe pair includes a capture probe and a reporter probe containing a unique fluorescent barcode for quantification. For each reaction, the pre-amplified RNA was hybridized with the Reporter CodeSet and Capture ProbeSet at 65°C for 16–18 hours, following the standard protocol. Before conducting the main experiments, we performed preliminary optimization tests under varying pre-amplification cycle numbers and hybridization durations. These trials indicated that hybridizing the pre-amplified RNA for 22 hours yielded the most robust and reproducible gene expression profiles. Data analysis was conducted using the ROSALIND® platform (Rosalind, Inc., San Diego, CA), which employs a HyperScale architecture. As part of quality control, ROSALIND generated read distribution metrics, violin plots, identity heatmaps, and multidimensional scaling (MDS) plots. Normalization, fold change calculation, and statistical testing followed NanoString’s recommended protocols. Specifically, the nCounter Advantage Analysis pipeline was applied, normalizing counts by dividing them by the geometric mean of selected housekeeping genes within each lane. Housekeeping genes confirmed to be expressed in neonatal saliva-GAPDH, HPRT1, and YWHAZ 39 -were used for normalization. Differential expression analysis was conducted using a fast methods statistical framework described in the nCounter Advantage Analysis 2.0 User Manual , which applies a generalized linear model to estimate fold changes and associated p-values. Multiple testing correction was performed using the Benjamini–Hochberg procedure to control the false discovery rate (FDR). Results with adjusted p -values ≥ 0.05 were retained for interpretation. This GLM-based approach is particularly well-suited for NanoString data as it accounts for the discrete nature of count data and accommodates technical variation, thereby enhancing the accuracy and reproducibility of differential expression estimates. GLMs were prespecified to include the following predictors: sex (male/female), prenatal opioid exposure (unexposed vs exposed), opioid medication type among exposed pregnancies (methadone vs buprenorphine), and whether the infant required postnatal pharmacologic therapy for neonatal opioid withdrawal syndrome (NOWS) (yes/no). For full-cohort analyses, exposure was encoded as a three-level factor (unexposed, methadone, buprenorphine) to avoid collinearity between exposure and medication type. We also conducted an exposed-only sensitivity analysis (methadone vs buprenorphine), adjusting for sex and NOWS therapy. Given the sample size, higher-order interactions were limited to the sex × exposure term, which was specified a priori. To assess sex-specific gene expression patterns, data were stratified by sex, and differential expression analysis was performed separately for male and female samples. This approach aimed to identify sex-specific differences in gene expression, particularly those potentially influenced by prenatal opioid exposure. Declarations Acknowledgements: We would like to thank all the families and infants who participated in the study. Author contributions: Conceptualization: T.K.-T., E.M.B., F.M.V., K.R.G., E.Y.; Methodology: T.K.-T., E.Y.; Data acquisition and analysis: F.C.-S., K.S., T.K.-T., E.Y., Draft preparation: T.K.-T., E.Y., Review and editing: T.K.-T., F.C.-S., K.S., K.R.G., E.M.B., F.M.V., E.Y.; Funding acquisition: E.M.B., F.M.V., E.Y. Funding: This work was funded by Tufts University Russo Family Award (E.M.B., F.M.V, E.Y.), Tufts Initiative on Substance Use and Addiction Award (E.M.B., E.Y.), NIDA K23 DA056847 (E.Y.) Data availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interest statement: All authors declare no competing interests. References Jarlenski, M. et al. Healthcare Patterns of Pregnant Women and Children Affected by OUD in 9 State Medicaid Populations. J. Addict. Med. 15 , 406–413 (2021). Klaman, S. L. et al. Treating Women Who Are Pregnant and Parenting for Opioid Use Disorder and the Concurrent Care of Their Infants and Children. J. Addict. Med. 11 , 178–190 (2017). Patrick, S. W. et al. Neonatal Abstinence Syndrome and Associated Health Care Expenditures: United States, 2000–2009. JAMA 307 , 1934–1940 (2012). Huybrechts, K. F. et al. Risk of neonatal drug withdrawal after intrauterine co-exposure to opioids and psychotropic medications: cohort study. BMJ 358 , j3326 (2017). Leech, A. A., Cooper, W. O., McNeer, E., Scott, T,. A. & Patrick, S. W. Neonatal Abstinence Syndrome In The United States, 2004–16. Heal. Aff. 39 , 764–767 (2020). Miele, K. et al. Medication for Opioid Use Disorder During Pregnancy — Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014–2021. MMWR Surveill. Summ. 72 , 1–14 (2023). Committee Opinion No. 711. Obstet. Gynecol. 130 , e81–e94 (2017). Andersen, J. M., Høiseth, G. & Nygaard, E. Prenatal exposure to methadone or buprenorphine and long-term outcomes: A meta-analysis. Early Hum. Dev. 143 , 104997 (2020). Mactier, H. & Hamilton, R. Prenatal opioid exposure – Increasing evidence of harm. Early Hum. Dev. 150 , 105188 (2020). Brogly, S. B., Saia, K. A., Walley, A. Y., Du, ,H. M. & Sebastiani, P. Prenatal Buprenorphine Versus Methadone Exposure and Neonatal Outcomes: Systematic Review and Meta-Analysis. Am. J. Epidemiology 180 , 673–686 (2014). Azuine, R. E. et al. Prenatal Risk Factors and Perinatal and Postnatal Outcomes Associated With Maternal Opioid Exposure in an Urban, Low-Income, Multiethnic US Population. JAMA Netw. Open 2 , e196405 (2019). VELEZ, M. L., JANSSON, L. M., SCHROEDER, J. & WILLIAMS, E. Prenatal Methadone Exposure and Neonatal Neurobehavioral Functioning. Pediatr. Res. 66 , 704–709 (2009). Velez, M. L. et al. Prenatal buprenorphine exposure and neonatal neurobehavioral functioning. Early Hum. Dev. 117 , 7–14 (2018). Charles, M. K. et al. Male Sex Associated With Increased Risk of Neonatal Abstinence Syndrome. Hosp. Pediatr. 7 , 328–334 (2017). O’Connor, A. B., O’Brien, L. & Alto, W. A. Are there gender related differences in neonatal abstinence syndrome following exposure to buprenorphine during pregnancy? jpme 41 , 621–623 (2013). McCarthy, M. M. Sex differences in the developing brain as a source of inherent risk. Dialogues Clin. Neurosci. 18 , 361–372 (2016). Maldonado, R. et al. Absence of opiate rewarding effects in mice lacking dopamine D2 receptors. Nature 388 , 586–589 (1997). Noble, E. P. Addiction and its reward process through polymorphisms of the D2 dopamine receptor gene: a review. Eur. Psychiatry 15 , 79–89 (2000). Zhang, P. et al. Toll-Like Receptor 4 (TLR4)/Opioid Receptor Pathway Crosstalk and Impact on Opioid Analgesia, Immune Function, and Gastrointestinal Motility. Front. Immunol. 11 , 1455 (2020). King’uyu, D. N. et al. The effect of morphine on rat microglial phagocytic activity: An in vitro study of brain region-, plating density-, sex-, morphine concentration-, and receptor-dependency. J. Neuroimmunol. 384 , 578204 (2023). Yen, E. et al. Sex-Dependent Gene Expression in Infants with Neonatal Opioid Withdrawal Syndrome. J Pediatrics 214 , 60–65.e2 (2019). Yen, E. et al. Sex-specific inflammatory and white matter effects of prenatal opioid exposure: a pilot study. Pediatr Res 1–8 (2022) doi: 10.1038/s41390-022-02357-5 . Elman, I. et al. Metabolic and Addiction Indices in Patients on Opioid Agonist Medication-Assisted Treatment: A Comparison of Buprenorphine and Methadone. Sci. Rep. 10 , 5617 (2020). Miller, N. W. et al. The impact of opioid exposure during pregnancy on the human neonatal immune profile. Pediatr. Res. 92 , 1566–1574 (2022). Newville, J., Maxwell, J. R., Kitase, Y., Robinson, S. & Jantzie, L. L. Perinatal Opioid Exposure Primes the Peripheral Immune System Toward Hyperreactivity. Front. Pediatr. 8 , 272 (2020). Ross, R. A. et al. Prefrontal cortex melanocortin 4 receptors (MC4R) mediate food intake behavior in male mice. Physiol. Behav. 269 , 114280 (2023). Yen, E. & Maron, J. L. Aberrant Feeding and Growth in Neonates With Prenatal Opioid Exposure: Evidence of Neuromodulation and Behavioral Changes. Front. Pediatr. 9 , 805763 (2022). Nugent, B. M., O’Donnell, C. M., Epperson, C. N. & Bale, T. L. Placental H3K27me3 establishes female resilience to prenatal insults. Nat. Commun. 9 , 2555 (2018). DiPietro, J. A. & Voegtline, K. M. The gestational foundation of sex differences in development and vulnerability. Neuroscience 342 , 4–20 (2017). E., J. H. et al. Neonatal Abstinence Syndrome after Methadone or Buprenorphine Exposure. N. Engl. J. Med. 363 , 2320–2331 (2010). Hall, E. S. et al. A Cohort Comparison of Buprenorphine versus Methadone Treatment for Neonatal Abstinence Syndrome. J. Pediatr. 170 , 39–44.e1 (2016). Jones, H. E. et al. Buprenorphine versus methadone in the treatment of pregnant opioid-dependent patients: effects on the neonatal abstinence syndrome. Drug Alcohol Depend. 79 , 1–10 (2005). Green, J. M., Sundman, M. H. & Chou, Y. Opioid-induced microglia reactivity modulates opioid reward, analgesia, and behavior. Neurosci. Biobehav. Rev. 135 , 104544 (2022). Sheikh, M. et al. Opium use and subsequent incidence of cancer: results from the Golestan Cohort Study. Lancet Glob. Heal. 8 , e649–e660 (2020). Sun, M., Lin, J.-A., Chang, C.-L., Wu, S.-Y. & Zhang, J. Association between long-term opioid use and cancer risk in patients with chronic pain: a propensity score-matched cohort study. Br. J. Anaesth. 129 , 84–91 (2022). Shrestha, S., Foot, H., Sheikh, M., Parat, M.-O. & Caze, A. L. Opioid use and the risk of cancer incidence and mortality: a systematic review. Cancer Metastasis Rev. 44 , 54 (2025). Dietz, J. A., Johnson, K. L., Wick, H. C., Bianchi, D. W. & Maron, J. L. Optimal Techniques for mRNA Extraction from Neonatal Salivary Supernatant. Neonatology 101 , 55 60 (2012). Yen, E., Kaneko-Tarui, T. & Maron, J. L. Technical Considerations and Protocol Optimization for Neonatal Salivary Biomarker Discovery and Analysis. Frontiers Pediatrics 8 , 618553 (2021). Khanna, P., Johnson, K. L. & Maron, J. L. Optimal reference genes for RT-qPCR normalization in the newborn. Biotech Histochem 92 , 459 466 (2017). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 20 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Editor invited by journal 11 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 08 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7418166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519397209,"identity":"3b7e6f67-2746-4136-b0cf-ce2b9fbf26c4","order_by":0,"name":"Tomoko Kaneko-Tarui","email":"data:image/png;base64,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","orcid":"","institution":"Tufts Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Tomoko","middleName":"","lastName":"Kaneko-Tarui","suffix":""},{"id":519397210,"identity":"f4939c4e-0e8b-4be9-950f-db1e3a6071b6","order_by":1,"name":"Francesca Carasi-Schwartz","email":"","orcid":"","institution":"Tufts Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Carasi-Schwartz","suffix":""},{"id":519397211,"identity":"fd9bff98-e00d-4309-8947-6641521466c1","order_by":2,"name":"Kiran Singh","email":"","orcid":"","institution":"Tufts Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Kiran","middleName":"","lastName":"Singh","suffix":""},{"id":519397212,"identity":"895e05ff-143f-4b51-9859-9c3e707fd08e","order_by":3,"name":"Kelsea R. Gildawie","email":"","orcid":"","institution":"Simmons University","correspondingAuthor":false,"prefix":"","firstName":"Kelsea","middleName":"R.","lastName":"Gildawie","suffix":""},{"id":519397213,"identity":"5c84f8a1-2254-4956-b9c3-56b909a2ed5f","order_by":4,"name":"Fair M. Vassoler","email":"","orcid":"","institution":"Tufts University Cummings School of Veterinary Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fair","middleName":"M.","lastName":"Vassoler","suffix":""},{"id":519397215,"identity":"ed9aa9bf-8b60-41da-898a-06c14707eccb","order_by":5,"name":"Elizabeth M. Byrnes","email":"","orcid":"","institution":"Tufts University Cummings School of Veterinary Medicine","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"M.","lastName":"Byrnes","suffix":""},{"id":519397217,"identity":"268e70a4-8f0a-4353-a7d7-ea9b1713d967","order_by":6,"name":"Elizabeth Yen","email":"","orcid":"","institution":"Tufts Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Yen","suffix":""}],"badges":[],"createdAt":"2025-08-20 13:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7418166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7418166/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-49873-6","type":"published","date":"2026-04-24T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92055164,"identity":"6d1ead39-a871-4b3b-8c58-7d9d5c776f81","added_by":"auto","created_at":"2025-09-24 06:44:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82183,"visible":true,"origin":"","legend":"","description":"","filename":"NASNanoStringFinalrev.docx","url":"https://assets-eu.researchsquare.com/files/rs-7418166/v1/ce17d8887c433899fb319ac2.docx"},{"id":92055163,"identity":"18123163-1798-490e-8b77-3092637e4d70","added_by":"auto","created_at":"2025-09-24 06:44:16","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8350,"visible":true,"origin":"","legend":"","description":"","filename":"2f06ddd173a14dffa08207f54704aad3.json","url":"https://assets-eu.researchsquare.com/files/rs-7418166/v1/875fe0f1a052c3e34bf7d71f.json"},{"id":92055165,"identity":"0e21c6bc-9f1f-4aff-a944-2b4f5ef3d378","added_by":"auto","created_at":"2025-09-24 06:44:16","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84817,"visible":true,"origin":"","legend":"","description":"","filename":"2f06ddd173a14dffa08207f54704aad31enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7418166/v1/790b5dd24a1091f348ce4a38.xml"},{"id":92055168,"identity":"0736a8b9-caa6-45c1-98af-f72fe641d825","added_by":"auto","created_at":"2025-09-24 06:44:16","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83024,"visible":true,"origin":"","legend":"","description":"","filename":"2f06ddd173a14dffa08207f54704aad31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7418166/v1/3d3fd4d953e77b43b27de796.xml"},{"id":92055166,"identity":"eb73a372-12d6-404c-b5b6-1b52b1bd08e4","added_by":"auto","created_at":"2025-09-24 06:44:16","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97572,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7418166/v1/3fcc56f56f4b7a5e74204731.html"},{"id":107927843,"identity":"af923ce6-93b7-445b-8ffe-421e0ab18ded","added_by":"auto","created_at":"2026-04-27 16:05:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":466851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7418166/v1/91d057dd-2953-4fa5-9b7f-937647061af8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Pilot Multiplex Salivary Transcriptomic Analysis to Understand the Sex-specific Effects of Maternal Opioid Use in Offspring","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe incidence of opioid use disorder (OUD) among pregnant individuals has steadily increased\u003csup\u003e1,2\u003c/sup\u003e, accompanied by a parallel surge in neonatal opioid withdrawal syndrome (NOWS). NOWS affects approximately 6–20 per 1,000 live births, depending on the geographic region and population\u003csup\u003e3–5\u003c/sup\u003e. Medications for opioid use disorder (MOUD), including methadone and buprenorphine, are the standard of care for pregnant individuals with OUD\u003csup\u003e6,7\u003c/sup\u003e. These opioid agonist therapies reduce illicit opioid use, improve maternal health, and promote prenatal care engagement. However, both drugs cross the placenta and can influence fetal neurodevelopment, potentially affecting neonatal feeding behavior, immune function, and long-term health outcomes\u003csup\u003e8–10\u003c/sup\u003e. In addition to the clinical management of acute withdrawal in the period after birth, prenatal opioid exposure is also associated with more chronic and long-term effects, including low birth weight, small-for-gestational-age (SGA) status, impaired postnatal growth, and neurodevelopmental delays\u003csup\u003e9,11–13\u003c/sup\u003e. Therefore, maternal OUD is a significant public health concern, with profound implications for neonatal health and developmental outcomes.\u003c/p\u003e\u003cp\u003eDespite the well-documented clinical manifestations and risks, the molecular mechanisms underlying neonatal responses to opioid exposure \u003cem\u003ein utero\u003c/em\u003e remain understudied. Emerging evidence from both human and animal studies suggests that sex-specific biological responses may play a critical role in shaping the trajectory of these outcomes. For instance, male neonates exposed to opioids \u003cem\u003ein utero\u003c/em\u003e are more vulnerable to severe NOWS and exhibit poorer cognitive and behavioral outcomes compared to opioid-exposed females\u003csup\u003e14,15\u003c/sup\u003e. These sex differences are likely driven by variations in immune, metabolic, and neurodevelopmental pathways\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent studies have begun to elucidate molecular pathways that may mediate these sex-specific effects of opioid exposure. One such pathway involves the dopamine receptor D2 (DRD2), a critical component of the brain’s reward signaling system\u003csup\u003e17,18\u003c/sup\u003e, which has been implicated in opioid-related reinforcement behaviors. In addition, animal models indicate that opioids can activate toll-like receptor 4 (TLR4) on microglia, triggering proinflammatory cascades that enhance opioid reward and may contribute to neuroinflammation\u003csup\u003e19,20\u003c/sup\u003e. Our laboratory was the first to demonstrate sex-specific effects of maternal opioid use on \u003cem\u003eDRD2\u003c/em\u003e expression, suggesting a potential mechanism for aberrant feeding behavior in neonates, particularly males\u003csup\u003e21\u003c/sup\u003e. We also demonstrated that upregulation of proinflammatory genes - interleukin-6 (\u003cem\u003eIL6\u003c/em\u003e), interleukin-1 beta (\u003cem\u003eIL1β\u003c/em\u003e), and tumor necrosis factor alpha (\u003cem\u003eTNFα\u003c/em\u003e) - might underlie white matter injury in opioid-exposed offspring, with greater effects observed in females than in males \u003csup\u003e22\u003c/sup\u003e. Together, these findings highlight that reward signaling and immune activation may be differentially regulated by opioids depending on neonatal sex.\u003c/p\u003e\u003cp\u003eBuilding on these data, the current pilot study aimed to identify additional candidate genes that may address the critical knowledge gap of understanding the sex-specific effects of maternal opioid use. Leveraging multiplex, high-throughput commercial techniques that have not been previously applied to neonatal saliva, we profiled gene pathways related to opioid use, including inflammation, reward, feeding regulation, oxidative stress, and energy metabolism \u003csup\u003e16–19,21–23\u003c/sup\u003e. In addition to opioid exposure and sex, we also explored whether molecular signatures vary by the types of maternal opioid use (methadone, buprenorphine).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\u003cp\u003eFollowing an initial quality check (QC) against the standard for multiplex transcriptomics and further optimization of the transcriptomic experiments (see Methods), samples from 18 neonates (nine opioid-exposed, nine non-exposed) passed the QC and were included in the final analysis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, opioid-exposed neonates were more likely to be SGA (33.3% vs. 0%) and born to mothers with hepatitis C (55.6% vs. 0%) compared to non-exposed neonates. Additionally, opioid-exposed neonates were smaller at birth (weight, length, and head circumference). There was a higher proportion of females in the non-exposed group (77.8%) relative to the opioid-exposed group (33.3%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Exposed (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOpioid-Exposed (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (77.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (77.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (88.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (88.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.0 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.6 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBW (grams)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2955.1 (433.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2721.4 (624.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBW percentile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.9 (49.0, 66.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.0 (4.0, 46.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.3 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.8 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength percentile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.0 (37.0, 73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.0 (10.0, 37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.7 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.0 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC percentile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.0 (60.0, 75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.0 (12.3, 49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean section\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-minute Apgar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.0 (8.0, 9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.0 (8.0, 8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5-minute Apgar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.0 (9.0, 9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.0 (8.0, 9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Hepatitis C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal GBS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal medication\u003c/p\u003e\u003cp\u003eBuprenorphine\u003c/p\u003e\u003cp\u003eMethadone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (77.8)\u003c/p\u003e\u003cp\u003e2 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolysubstance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNA\u0026thinsp;=\u0026thinsp;not available/applicable, GA\u0026thinsp;=\u0026thinsp;gestational age, BW\u0026thinsp;=\u0026thinsp;birth weight, HC\u0026thinsp;=\u0026thinsp;head circumference, SGA\u0026thinsp;=\u0026thinsp;small for gestational age. Data are presented as mean (standard deviation) or median (interquartile ranges) for continuous measures, and N (%) for categorical measures. Bolded p values represent significant differences.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGene Expression Differences by Opioid Exposure\u003c/h3\u003e\n\u003cp\u003eComparing opioid-exposed with non-exposed neonates, gene expression analyses revealed significant dysregulation in several inflammation- and neurodevelopment-related genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, \u003cem\u003ePTGS2\u003c/em\u003e (\u003cem\u003eCOX 2\u003c/em\u003e), a key mediator of inflammation, was significantly upregulated in the exposed group (fold change: 3.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). In contrast, expression of \u003cem\u003eCCL2\u003c/em\u003e and \u003cem\u003eBDNF\u003c/em\u003e was downregulated (fold changes: \u0026minus;\u0026thinsp;3.42 and \u0026minus;\u0026thinsp;2.38, respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting suppressed immune regulation and neuronal signaling.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferential Gene Expression by Opioid Exposure\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBy Exposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbbreviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePathways/diseases*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFold Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003cp\u003e(9 exp. vs 9 non-exp.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCCL2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC-C motif chemokine ligand 2, monocyte chemoattractant protein-1 (\u003cem\u003eMCP-1\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmunoregulatory, inflammation, neurodegenerative disorder, cancer, cardiovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBrain derived neurotrophic factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeuronal development, cognition, memory, learning, neuropsychiatry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePTGS2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProstaglandin-endoperoxide synthase 2, cyclooxygenase 2 (\u003cem\u003eCOX 2\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInflammation, prostaglandin biosynthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003cp\u003e(3 exp. vs 7 non-exp.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCCL2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC-C motif chemokine ligand 2, monocyte chemoattractant protein-1 (\u003cem\u003eMCP-1\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmunoregulatory, inflammation, neurodegenerative disorder, cancer, cardiovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCXCR4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC-X-C motif chemokine receptor 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmune response, neurological functions, cancer progression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIL6\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterleukin 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInflammation, immune response, cancer progression, obesity, diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003cp\u003e(6 exp. vs 2 non-exp.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDRD2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDopamine receptor 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReward sensitivity, locomotion, cognition, emotion regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRELA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREL proto-oncogene, NF-ĸB subunit, p65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOxidative stress, inflammation, autoimmune disorders, cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMC4R\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMelanocortin 4 receptor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnergy homeostasis, body weight and feeding regulation, obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFOS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFos proto-oncogene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmune response, inflammation, cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePTGS2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProstaglandin-endoperoxide synthase 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInflammation, prostaglandin biosynthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSQSTM1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSequestosome 1 (p62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmunity, neurodegenerative disorders, cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBy Opioid Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGenes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eAbbreviation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePathways/diseases*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eFold Change\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 Methadone vs. 7 Buprenorphine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCXCR4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC-X-C motif chemokine receptor 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmune response, neurological functions, cancer progression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTNF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTumor necrosis factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeuronal development, cognition, memory, learning, neuropsychiatry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSQSTM1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSequestosome 1 (p62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmunity, neurodegenerative disorders, cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003e*\u003c/b\u003esource: Rosalind\u0026reg; Academy version 3.39.13.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rosalind.bio/academy\u003c/span\u003e\u003cspan address=\"https://rosalind.bio/academy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); exp: opioid-exposed\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStratified analyses revealed marked sex-differential effects of maternal opioid use. Within the female cohort, maternal opioid use downregulated inflammation-related genes, including \u003cem\u003eCCL2\u003c/em\u003e, \u003cem\u003eCXCR4\u003c/em\u003e, and \u003cem\u003eIL6\u003c/em\u003e (fold changes: \u0026minus;\u0026thinsp;8.14 to \u0026minus;\u0026thinsp;5.29; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.05). In contrast, in the male cohort, maternal opioid use upregulated \u003cem\u003eDRD2\u003c/em\u003e, \u003cem\u003eRELA\u003c/em\u003e, \u003cem\u003eMC4R, FOS, PTGS2\u003c/em\u003e, and \u003cem\u003eSQSTM1\u003c/em\u003e (fold changes: 3.14 to 6.04; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u0026ndash;0.05). These genes are implicated in reward signaling, oxidative stress response, feeding and energy regulation, as well as cognition and neurodegenerative disorders. Interestingly, cancer was also involved in several of these pathways.\u003c/p\u003e\n\u003ch3\u003eDifferences by Types of Opioid Exposure\u003c/h3\u003e\n\u003cp\u003eComparisons between neonates exposed to methadone (n\u0026thinsp;=\u0026thinsp;2) versus buprenorphine (n\u0026thinsp;=\u0026thinsp;7) showed that methadone was associated with significantly greater expression of immune-related genes. Specifically, \u003cem\u003eCXCR4\u003c/em\u003e and \u003cem\u003eTNF\u003c/em\u003e were upregulated in the methadone group (fold changes: 9.38 and 5.40; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as was \u003cem\u003eSQSTM1\u003c/em\u003e, a gene involved in autophagy, immunity, and neurodegenerative disorders (fold change: 3.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A few of these pathways were associated with cancer.\u003c/p\u003e\n\u003ch3\u003eSex-Specific Differences in Gene Expression\u003c/h3\u003e\n\u003cp\u003eIndependent of exposure status, sex-stratified analysis across all neonates showed that males had lower expression of metabolic genes such as \u003cem\u003ePPARα\u003c/em\u003e, \u003cem\u003eAKT1\u003c/em\u003e, and \u003cem\u003eAMPK\u003c/em\u003e compared to females (fold changes: \u0026minus;\u0026thinsp;4.10 to \u0026minus;\u0026thinsp;2.46; all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and greater expression of prostaglandin biosynthesis factor \u003cem\u003ePTGS2\u003c/em\u003e, angiogenic and atherosclerotic factor \u003cem\u003eVEGFα\u003c/em\u003e, and metabolic homeostasis \u003cem\u003eLEPR\u003c/em\u003e (fold changes: 2.5 to 3.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eAmong non-exposed neonates, \u003cem\u003eRELA\u003c/em\u003e was downregulated in males compared to females (fold change: \u0026minus;\u0026thinsp;4.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), suggesting a lower baseline inflammatory state. However, in opioid-exposed neonates, males showed substantial upregulation of inflammatory and immune pathway genes, including \u003cem\u003ePTGS2\u003c/em\u003e, \u003cem\u003eTLR4\u003c/em\u003e, \u003cem\u003eIL1β\u003c/em\u003e, \u003cem\u003eCXCR4\u003c/em\u003e, and \u003cem\u003eCXCL8\u003c/em\u003e (fold changes: 2.20 to 6.50; all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), underscoring a sex-specific activation of immune and neuroinflammatory pathways in response to maternal opioid use. Cancer progression again presented and paralleled the inflammation in some of these pathways (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferential Gene Expression by Sex\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBy Sex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbbreviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePathways/diseases*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFold Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003cp\u003e(8 males vs 10 females)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePPARA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeroxisome proliferator activated receptor alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnergy, cholesterol, and lipid metabolism, glucose homeostasis, immune, inflammation, obesity, atherosclerosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAKT1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAKT serine/threonine kinase 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCell metabolism, insulin signaling, cancer,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMPK\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAMP-activated protein kinase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnergy metabolism, fatty acid oxidation, inflammation, diabetes, obesity, metabolic disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePTGS2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProstaglandin-endoperoxide synthase 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInflammation, prostaglandin biosynthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eVEGFA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVascular endothelial growth factor A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAngiogenesis, atherosclerosis, hypoxic-induced neovascularization, obesity, tumor growth, cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLEPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLeptin receptor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnergy homeostasis, metabolism, insulin signaling, glucose homeostasis, obesity, cancer, metabolic syndrome, cardiovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-exp.\u003c/p\u003e\u003cp\u003e(2 males vs 7 females)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRELA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREL proto-oncogene, NFĸB subunit, p65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOxidative stress, inflammation, autoimmune disorders, cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExp.\u003c/p\u003e\u003cp\u003e(6 males vs 3 females)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePTGS2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProstaglandin-endoperoxide synthase 2, cyclooxygenase 2 (\u003cem\u003eCOX 2\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInflammation, prostaglandin biosynthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTLR4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eToll-like receptor 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnate immunity, pathogen recognition, inflammation, microglia activation, neuroinflammation, cancer, autoimmune disorders, neurodegeneration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIL1B\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterleukin 1 beta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInflammation, neuroinflammation, cancer, diabetes, bipolar disorder, Alzheimer\u0026rsquo;s disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCXCR4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC-X-C motif chemokine receptor 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmune response, neurological functions, cancer progression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCXCL8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC-X-C motif ligand 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInflammation, cancer, neurodegeneration, neuroinflammation, Parkinson\u0026rsquo;s disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003e*\u003c/b\u003esource: Rosalind\u0026reg; Academy version 3.39.13.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rosalind.bio/academy\u003c/span\u003e\u003cspan address=\"https://rosalind.bio/academy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); exp: opioid-exposed\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis pilot study provides novel evidence of sex-specific molecular responses in neonates prenatally exposed to maternal methadone and buprenorphine. To our knowledge, this study is the first to leverage a high-throughput, commercial multiomic profiling platform for neonatal saliva using the nCounter® Analysis System (NanoString Technologies, Seattle, WA, USA). By adapting this platform for saliva, we identified distinct patterns in inflammation, reward signaling, and energy regulation pathways, offering insight into the biological mechanisms underlying differential neonatal vulnerability.\u003c/p\u003e\u003cp\u003eOur findings support and extend prior observations from animal models and emerging human data, while introducing neonatal saliva as a feasible biospecimen for molecular analysis. Miller et al. reported reduced neutrophils and inflammatory cytokines in the umbilical cord blood of opioid-exposed neonates compared to those in non-exposed neonates\u003csup\u003e24\u003c/sup\u003e. In contrast, Newville et al. observed elevated proinflammatory cytokines and chemokines, along with heightened immune reactivity, in methadone-exposed rats\u003csup\u003e25\u003c/sup\u003e. Our results integrate these findings by showing that maternal opioid use modulates inflammatory responses in offspring, with the direction and magnitude of changes differing by sex. Specifically, the downregulation of \u003cem\u003eCCL2\u003c/em\u003e and \u003cem\u003eBDNF\u003c/em\u003e expression, alongside the upregulation of \u003cem\u003ePTGS2\u003c/em\u003e, indicates that the inflammatory responses following \u003cem\u003ein utero\u003c/em\u003e opioid exposure represent a complex interplay of biological and environmental factors. For example, the female-predominant downregulation of CCL2 may underlie the overall reduced expression of this gene in the exposed cohort. In contrast, the increased expression of PTGS2 in exposed males likely drives the elevated levels observed. Thus, sex appears to mediate the inflammatory consequences of maternal opioid use, as evidenced by male-versus female-specific molecular alterations identified in this study. These alterations span pathways related to inflammation, immune regulation, neurodevelopment, cognition, metabolic homeostasis, and neurodegenerative risk.\u003c/p\u003e\u003cp\u003eTherefore, our pilot study highlights the need to include sex as a biological variable in understanding the impact of maternal opioid use on offspring. The greater expression of \u003cem\u003eDRD2\u003c/em\u003e in opioid-exposed males in the current study aligns with our published data showing the male-predominant effects of opioids on heightened reward signaling and feeding behavior\u003csup\u003e19\u003c/sup\u003e. Of interest is the greater expression of \u003cem\u003eMC4R\u003c/em\u003e in opioid-exposed males. Given that \u003cem\u003eMC4R\u003c/em\u003e is critical for feeding and metabolism and is typically upregulated in a satiated state\u003csup\u003e26\u003c/sup\u003e, the concurrent increase in \u003cem\u003eDRD2\u003c/em\u003e and \u003cem\u003eMC4R\u003c/em\u003e expression in opioid-exposed neonates suggests that maternal opioid use dysregulates the hypothalamic balance and may explain the feeding dysregulation often observed in these neonates\u003csup\u003e27\u003c/sup\u003e. The elevated expression of \u003cem\u003eRELA\u003c/em\u003e and \u003cem\u003eMC4R\u003c/em\u003e further implicates oxidative stress and disrupted energy homeostasis in male neonates, consistent with studies linking prenatal opioid exposure to metabolic dysregulation\u003csup\u003e27\u003c/sup\u003e and long-term neurobehavioral consequences\u003csup\u003e9,11–13\u003c/sup\u003e. While our data need to be validated in a larger sample size, these findings provide a critical foundation to link sex-specific molecular changes with clinical presentations in opioid-exposed neonates to arrive at effective interventions and personalized care.\u003c/p\u003e\u003cp\u003eStratification by sex (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) provides further evidence of the distinct molecular response to \u003cem\u003ein utero\u003c/em\u003e opioid exposure between male and female neonates. Opioid-exposed males exhibited upregulation of inflammation, neuroinflammation, and neurodegenerative disorders as shown by the greater expression of \u003cem\u003eTLR4, PTGS2, IL1β, CXCR4\u003c/em\u003e, and \u003cem\u003eCXCL8\u003c/em\u003e. While opioids act via opioid receptors, animal data have demonstrated the non-neuronal effects of opioids by binding with TLR4 in microglia and the release of chemokines and cytokines, and subsequent reinforcement of reward signaling\u003csup\u003e18\u003c/sup\u003e. Our study is the first to suggest that maternal opioid use may modulate inflammation through its sex-specific effect on the TLR4 pathway, which may explain the upregulation of \u003cem\u003eDRD2\u003c/em\u003e in opioid-exposed males.\u003c/p\u003e\u003cp\u003eIn contrast, opioid-exposed females showed marked downregulation of genes in the inflammation and immune pathways. Our findings align with previous evidence that female neonates may possess intrinsic immunological advantages and resilience to prenatal stressors\u003csup\u003e28\u003c/sup\u003e and that male sex confers a greater vulnerability to adverse prenatal and perinatal effects\u003csup\u003e29\u003c/sup\u003e. The directionality and magnitude of these differences point to a biologically plausible framework in which inflammation and reward circuits are differentially modulated by sex, potentially influencing the severity of NOWS and future neurodevelopment in the offspring.\u003c/p\u003e\u003cp\u003eAnother important observation, albeit limited by our small sample size, is the differential gene expression based on the type of maternal opioid use. Neonates exposed to methadone demonstrated upregulation of genes in the inflammatory and immune pathways compared to those exposed to buprenorphine. Based on studies showing worse clinical outcomes in neonates exposed to methadone relative to buprenorphine (e.g., more severe NOWS and more extended hospital stays)\u003csup\u003e30–32\u003c/sup\u003e, the molecular data presented here may suggest a biological basis for these clinical trends.\u003c/p\u003e\u003cp\u003eThis study is the first to use neonatal saliva on a high-throughput, commercial transcriptomic platform. Despite the initial assay challenges, our team successfully optimized the experimental conditions to generate the current data. The technical descriptions reported in this study serve as proof of concept that using micro quantities of neonatal saliva to conduct multiplexed gene analyses is feasible and even desirable. Neonatal research is often limited by the types of procedures that can be used to generate data; therefore, the field must leverage the least invasive method possible to ensure safe and robust research in this population.\u003c/p\u003e\u003cp\u003eIn addition to the technical novelty, our molecular findings provide intriguing findings that can be utilized in future studies examining the mechanistic and clinical relevance of maternal opioid use in offspring. First, our results reinforce the role of \u003cem\u003eTLR4\u003c/em\u003e in opioid-related neuroimmune interactions, consistent with animal data showing microglial activation and inflammatory priming following opioid exposure\u003csup\u003e19,33\u003c/sup\u003e. Second, the greater expression of \u003cem\u003eDRD2\u003c/em\u003e may underscore the higher OUD prevalence in adult males than in females. In other words, the heightened reward signaling early in life may predispose to future reward-seeking behaviors, such as substance use or other types of addiction, with differential sex effects. Third, the consistent enrichment of metabolism-related pathways among opioid-exposed neonates—especially males—needs to be further studied as these early metabolic alterations may contribute to the lower birth weight, aberrant feeding behavior, and growth impairment observed in this population, all of which could lead to cardiometabolic issues in adulthood. Fourth, while the significance of cancer involvement in the pathways affected by maternal opioid use is unclear, future longitudinal studies should examine the risk of malignancy related to \u003cem\u003ein utero\u003c/em\u003e and childhood opioid exposure. This is especially important given the higher risk of cancer and cancer-related mortality in people with chronic opioid use\u003csup\u003e34–36\u003c/sup\u003e, although a meta-analysis cautioned against the risk of bias in overestimating the overall effect of opioid use on cancer outcomes\u003csup\u003e36\u003c/sup\u003e. Finally, using drops of neonatal saliva, our pilot study highlights the importance of including sex as a biological variable. The current data further support other studies from our group and others, demonstrating the sex-specific risks related to OUD and its impact on offspring. Understanding the dimorphic effects of sex can personalize the care of neonatal opioid withdrawal, which currently is based on a one-size-fits-all paradigm. From a translational perspective, these results highlight the potential for salivary biomarkers to identify neonates at higher risk of adverse outcomes. Such markers could guide personalized care strategies, including closer neurodevelopmental follow-up or early behavioral interventions.\u003c/p\u003e\u003cp\u003eLimitations of the current study include the small sample size, which reduces statistical power and generalizability. The use of saliva, while non-invasive and clinically practical, captures a subset of systemic molecular activity and may not fully reflect brain-specific processes. Additionally, potential confounding by maternal comorbidities (e.g., hepatitis C, polysubstance use) and unmeasured environmental exposures cannot be excluded. Finally, the cross-sectional design precludes conclusions about long-term consequences, underscoring the need for longitudinal follow-up.\u003c/p\u003e\u003cp\u003eFuture research should expand sample sizes, include additional tissue types such as cord blood or placenta, neuroimaging data, and developmental screening tests to better understand the associations with neurodevelopmental outcomes, as well as comprehensive cardiometabolic evaluations, including blood pressure and anthropometric measurements. Integrating multi-omics approaches—including epigenetics and proteomics—may further elucidate sex-specific biological programming in opioid-exposed neonates. Importantly, future studies should evaluate whether salivary gene expression profiles can serve as reliable predictors of clinical outcomes or treatment response in neonates with NOWS. Our study also serves as a foundation for a collaboration with preclinical researchers using non-human models to examine the mechanistic underpinnings of maternal opioid use.\u003c/p\u003e\u003cp\u003eIn conclusion, maternal opioid use is associated with distinct, sex-specific molecular responses in human neonates, with opioid-exposed males showing increased activation of inflammatory and reward-related pathways, and females displaying relative downregulation of immune signaling. These findings suggest that biological sex plays a critical role in shaping neonatal vulnerability to \u003cem\u003ein utero\u003c/em\u003e opioid exposure, potentially contributing to the variable severity of NOWS and long-term developmental outcomes.\u003c/p\u003e\u003cp\u003eThe observed upregulation of \u003cem\u003eDRD2\u003c/em\u003e and \u003cem\u003eTLR4\u003c/em\u003e in opioid-exposed males provides compelling evidence for a mechanistic link between immune activation and altered reward sensitivity, consistent with animal models of opioid reinforcement. Moreover, differential expression patterns based on the type of opioid—methadone versus buprenorphine—highlight the importance of individualized approaches to maternal treatment.\u003c/p\u003e\u003cp\u003eSaliva-based gene expression profiling offers a promising, non-invasive tool to uncover early biomarkers of risk, which may inform sex- and exposure-specific clinical monitoring and intervention strategies. Larger, longitudinal studies are needed to validate these findings and explore their relevance to neurodevelopmental trajectories, ultimately advancing personalized care for opioid-exposed neonates.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\n"},{"header":"Methods","content":"\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis pilot observational study used a prospective cohort design to investigate differences in gene expression between male and female neonates, comparing those with prenatal opioid exposure to those without. A total of 18 neonates were enrolled from the newborn nursery and the neonatal intensive care unit of Tufts Medical Center in Boston, Massachusetts, between February and November 2023. The study cohort included nine neonates with documented maternal opioid use during pregnancy (opioid-exposed group) and nine neonates without such exposure (non-exposed group). Neonates were matched by gestational age to the extent possible.\u003c/p\u003e\u003cp\u003eInclusion criteria for the opioid-exposed group included: (1) maternal opioid use confirmed by medical record documentation or toxicology screening, and (2) availability of saliva samples collected within 48 hours of birth. The non-exposed group consisted of neonates born to mothers with no known substance use during pregnancy and no detectable opioids in toxicology screening. Exclusion criteria included: major congenital anomalies, perinatal infection, or gestational age \u0026lt; 34 weeks.\u003c/p\u003e\u003cp\u003eMaternal demographic data (including race, ethnicity, delivery type, opioid type, group B streptococcus/GBS colonization status, hepatitis C status, cigarette smoking status) and neonatal clinical characteristics (gestational age, sex, birth weight, length, head circumference and corresponding percentiles, Apgar 1 and 5 minutes, SGA status) were obtained from the medical record.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e The study was approved by the Tufts Medical Center Institutional Review Board. All procedures were performed in accordance with institutional and national research committee ethical standards, the Declaration of Helsinki, and relevant guidelines and regulations. Written informed consent for study participation was obtained from a parent or legal guardian of each neonate prior to enrollment. No identifiable images or personal data are included in this manuscript, and all HIPAA identifiers have been removed.\u003c/p\u003e\u003ch3\u003eSample Collection and RNA Extraction\u003c/h3\u003e\u003cp\u003eSaliva samples were collected from neonates within 48 hours of birth using our established techniques\u003csup\u003e37\u003c/sup\u003e. Briefly, saliva was collected using a 1-milliliter (mL) insulin syringe (Becton, Dickinson and Company, Franklin Lakes, NJ) attached to the low-pressure wall suction for 15–30 seconds. To minimize breast milk and associated maternal RNA contamination, saliva was collected before feeding or at least 30 minutes after feeding. Saliva was immediately placed in 250 microliter (µL) RNAprotect Saliva Reagent (Qiagen, Hilden, Germany) to minimize RNA degradation. Total RNA was isolated using the RNeasy Micro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocols, which were optimized for low-input, saliva-derived specimens. On-column DNase treatment was performed using RNase-free DNase I (Qiagen) to minimize DNA contamination. Once RNA was extracted, the total RNA was stored at -80⁰C pending gene expression analysis.\u003c/p\u003e\u003ch2\u003eGene Expression Analysis\u003c/h2\u003e\u003cp\u003emRNA expression profiling was performed using the NanoString nCounter Analysis System (NanoString Technologies, Seattle, WA), a direct, multiplexed platform for quantifying gene expression. A custom CodeSet was designed to target 72 genes of interest, including selected housekeeping genes used for normalization.\u003c/p\u003e\u003cp\u003eTo prepare samples for hybridization, the NanoString Low RNA Input Kit (NanoString Technologies, Seattle, WA) was used according to the manufacturer's instructions. We subsequently determined that eight pre-amplification cycles were optimal for amplifying the mRNA transcripts using neonatal saliva, which is known to have a low starting mRNA level\u003csup\u003e38\u003c/sup\u003e. The nCounter assay employs a molecular barcoding system, in which specific probes hybridize directly to target mRNAs. Each probe pair includes a capture probe and a reporter probe containing a unique fluorescent barcode for quantification. For each reaction, the pre-amplified RNA was hybridized with the Reporter CodeSet and Capture ProbeSet at 65°C for 16–18 hours, following the standard protocol. Before conducting the main experiments, we performed preliminary optimization tests under varying pre-amplification cycle numbers and hybridization durations. These trials indicated that hybridizing the pre-amplified RNA for 22 hours yielded the most robust and reproducible gene expression profiles.\u003c/p\u003e\u003cp\u003eData analysis was conducted using the ROSALIND® platform (Rosalind, Inc., San Diego, CA), which employs a HyperScale architecture. As part of quality control, ROSALIND generated read distribution metrics, violin plots, identity heatmaps, and multidimensional scaling (MDS) plots. Normalization, fold change calculation, and statistical testing followed NanoString’s recommended protocols. Specifically, the nCounter Advantage Analysis pipeline was applied, normalizing counts by dividing them by the geometric mean of selected housekeeping genes within each lane. Housekeeping genes confirmed to be expressed in neonatal saliva-GAPDH, HPRT1, and YWHAZ\u003csup\u003e39\u003c/sup\u003e -were used for normalization.\u003c/p\u003e\u003cp\u003eDifferential expression analysis was conducted using a fast methods statistical framework described in the \u003cem\u003enCounter Advantage Analysis 2.0 User Manual\u003c/em\u003e, which applies a generalized linear model to estimate fold changes and associated p-values. Multiple testing correction was performed using the Benjamini–Hochberg procedure to control the false discovery rate (FDR). Results with adjusted \u003cem\u003ep\u003c/em\u003e-values ≥ 0.05 were retained for interpretation. This GLM-based approach is particularly well-suited for NanoString data as it accounts for the discrete nature of count data and accommodates technical variation, thereby enhancing the accuracy and reproducibility of differential expression estimates.\u003c/p\u003e\u003cp\u003eGLMs were prespecified to include the following predictors: sex (male/female), prenatal opioid exposure (unexposed vs exposed), opioid medication type among exposed pregnancies (methadone vs buprenorphine), and whether the infant required postnatal pharmacologic therapy for neonatal opioid withdrawal syndrome (NOWS) (yes/no). For full-cohort analyses, exposure was encoded as a three-level factor (unexposed, methadone, buprenorphine) to avoid collinearity between exposure and medication type. We also conducted an exposed-only sensitivity analysis (methadone vs buprenorphine), adjusting for sex and NOWS therapy. Given the sample size, higher-order interactions were limited to the sex × exposure term, which was specified a priori.\u003c/p\u003e\u003cp\u003eTo assess sex-specific gene expression patterns, data were stratified by sex, and differential expression analysis was performed separately for male and female samples. This approach aimed to identify sex-specific differences in gene expression, particularly those potentially influenced by prenatal opioid exposure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to thank all the families and infants who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: T.K.-T., E.M.B., F.M.V., K.R.G., E.Y.; Methodology: T.K.-T., E.Y.; Data acquisition and analysis: F.C.-S., K.S., T.K.-T., E.Y., Draft preparation: T.K.-T., E.Y., Review and editing: T.K.-T., F.C.-S., K.S., K.R.G., E.M.B., F.M.V., E.Y.; Funding acquisition: E.M.B., F.M.V., E.Y.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work\u0026nbsp;was\u0026nbsp;funded by\u0026nbsp;Tufts University Russo Family Award (E.M.B., F.M.V, E.Y.), Tufts Initiative on Substance Use and Addiction Award (E.M.B., E.Y.), NIDA\u0026nbsp;K23 DA056847 (E.Y.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest statement:\u0026nbsp;\u003c/strong\u003eAll authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJarlenski, M. \u003cem\u003eet al.\u003c/em\u003e Healthcare Patterns of Pregnant Women and Children Affected by OUD in 9 State Medicaid Populations. \u003cem\u003eJ. Addict. Med.\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 406\u0026ndash;413 (2021).\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKlaman, S. L. \u003cem\u003eet al.\u003c/em\u003e Treating Women Who Are Pregnant and Parenting for Opioid Use Disorder and the Concurrent Care of Their Infants and Children. \u003cem\u003eJ. Addict. Med.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 178\u0026ndash;190 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePatrick, S. W. \u003cem\u003eet al.\u003c/em\u003e Neonatal Abstinence Syndrome and Associated Health Care Expenditures: United States, 2000\u0026ndash;2009. \u003cem\u003eJAMA\u003c/em\u003e\u003cstrong\u003e307\u003c/strong\u003e, 1934\u0026ndash;1940 (2012).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHuybrechts, K. F. \u003cem\u003eet al.\u003c/em\u003e Risk of neonatal drug withdrawal after intrauterine co-exposure to opioids and psychotropic medications: cohort study. \u003cem\u003eBMJ\u003c/em\u003e\u003cstrong\u003e358\u003c/strong\u003e, j3326 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLeech, A. A., Cooper, W. O., McNeer, E., Scott, T,. A. \u0026amp; Patrick, S. W. Neonatal Abstinence Syndrome In The United States, 2004\u0026ndash;16. \u003cem\u003eHeal. Aff.\u003c/em\u003e\u003cstrong\u003e39\u003c/strong\u003e, 764\u0026ndash;767 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMiele, K. \u003cem\u003eet al.\u003c/em\u003e Medication for Opioid Use Disorder During Pregnancy \u0026mdash; Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014\u0026ndash;2021. \u003cem\u003eMMWR Surveill. Summ.\u003c/em\u003e\u003cstrong\u003e72\u003c/strong\u003e, 1\u0026ndash;14 (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCommittee Opinion No. 711. \u003cem\u003eObstet. Gynecol.\u003c/em\u003e\u003cstrong\u003e130\u003c/strong\u003e, e81\u0026ndash;e94 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAndersen, J. M., H\u0026oslash;iseth, G. \u0026amp; Nygaard, E. Prenatal exposure to methadone or buprenorphine and long-term outcomes: A meta-analysis. \u003cem\u003eEarly Hum. Dev.\u003c/em\u003e\u003cstrong\u003e143\u003c/strong\u003e, 104997 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMactier, H. \u0026amp; Hamilton, R. Prenatal opioid exposure \u0026ndash; Increasing evidence of harm. \u003cem\u003eEarly Hum. Dev.\u003c/em\u003e\u003cstrong\u003e150\u003c/strong\u003e, 105188 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBrogly, S. B., Saia, K. A., Walley, A. Y., Du, ,H. M. \u0026amp; Sebastiani, P. Prenatal Buprenorphine Versus Methadone Exposure and Neonatal Outcomes: Systematic Review and Meta-Analysis. \u003cem\u003eAm. J. Epidemiology\u003c/em\u003e\u003cstrong\u003e180\u003c/strong\u003e, 673\u0026ndash;686 (2014).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAzuine, R. E. \u003cem\u003eet al.\u003c/em\u003e Prenatal Risk Factors and Perinatal and Postnatal Outcomes Associated With Maternal Opioid Exposure in an Urban, Low-Income, Multiethnic US Population. \u003cem\u003eJAMA Netw. Open\u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, e196405 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVELEZ, M. L., JANSSON, L. M., SCHROEDER, J. \u0026amp; WILLIAMS, E. Prenatal Methadone Exposure and Neonatal Neurobehavioral Functioning. \u003cem\u003ePediatr. Res.\u003c/em\u003e\u003cstrong\u003e66\u003c/strong\u003e, 704\u0026ndash;709 (2009).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVelez, M. L. \u003cem\u003eet al.\u003c/em\u003e Prenatal buprenorphine exposure and neonatal neurobehavioral functioning. \u003cem\u003eEarly Hum. Dev.\u003c/em\u003e\u003cstrong\u003e117\u003c/strong\u003e, 7\u0026ndash;14 (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCharles, M. K. \u003cem\u003eet al.\u003c/em\u003e Male Sex Associated With Increased Risk of Neonatal Abstinence Syndrome. \u003cem\u003eHosp. Pediatr.\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 328\u0026ndash;334 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connor, A. B., O\u0026rsquo;Brien, L. \u0026amp; Alto, W. A. Are there gender related differences in neonatal abstinence syndrome following exposure to buprenorphine during pregnancy? \u003cem\u003ejpme\u003c/em\u003e\u003cstrong\u003e41\u003c/strong\u003e, 621\u0026ndash;623 (2013).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMcCarthy, M. M. Sex differences in the developing brain as a source of inherent risk. \u003cem\u003eDialogues Clin. Neurosci.\u003c/em\u003e\u003cstrong\u003e18\u003c/strong\u003e, 361\u0026ndash;372 (2016).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMaldonado, R. \u003cem\u003eet al.\u003c/em\u003e Absence of opiate rewarding effects in mice lacking dopamine D2 receptors. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e388\u003c/strong\u003e, 586\u0026ndash;589 (1997).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNoble, E. P. Addiction and its reward process through polymorphisms of the D2 dopamine receptor gene: a review. \u003cem\u003eEur. Psychiatry\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 79\u0026ndash;89 (2000).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhang, P. \u003cem\u003eet al.\u003c/em\u003e Toll-Like Receptor 4 (TLR4)/Opioid Receptor Pathway Crosstalk and Impact on Opioid Analgesia, Immune Function, and Gastrointestinal Motility. \u003cem\u003eFront. Immunol.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 1455 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKing\u0026rsquo;uyu, D. N. \u003cem\u003eet al.\u003c/em\u003e The effect of morphine on rat microglial phagocytic activity: An in vitro study of brain region-, plating density-, sex-, morphine concentration-, and receptor-dependency. \u003cem\u003eJ. Neuroimmunol.\u003c/em\u003e\u003cstrong\u003e384\u003c/strong\u003e, 578204 (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYen, E. \u003cem\u003eet al.\u003c/em\u003e Sex-Dependent Gene Expression in Infants with Neonatal Opioid Withdrawal Syndrome. \u003cem\u003eJ Pediatrics\u003c/em\u003e\u003cstrong\u003e214\u003c/strong\u003e, 60\u0026ndash;65.e2 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYen, E. \u003cem\u003eet al.\u003c/em\u003e Sex-specific inflammatory and white matter effects of prenatal opioid exposure: a pilot study. \u003cem\u003ePediatr Res\u003c/em\u003e 1\u0026ndash;8 (2022) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41390-022-02357-5\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eElman, I. \u003cem\u003eet al.\u003c/em\u003e Metabolic and Addiction Indices in Patients on Opioid Agonist Medication-Assisted Treatment: A Comparison of Buprenorphine and Methadone. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 5617 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMiller, N. W. \u003cem\u003eet al.\u003c/em\u003e The impact of opioid exposure during pregnancy on the human neonatal immune profile. \u003cem\u003ePediatr. Res.\u003c/em\u003e\u003cstrong\u003e92\u003c/strong\u003e, 1566\u0026ndash;1574 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNewville, J., Maxwell, J. R., Kitase, Y., Robinson, S. \u0026amp; Jantzie, L. L. Perinatal Opioid Exposure Primes the Peripheral Immune System Toward Hyperreactivity. \u003cem\u003eFront. Pediatr.\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 272 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRoss, R. A. \u003cem\u003eet al.\u003c/em\u003e Prefrontal cortex melanocortin 4 receptors (MC4R) mediate food intake behavior in male mice. \u003cem\u003ePhysiol. Behav.\u003c/em\u003e\u003cstrong\u003e269\u003c/strong\u003e, 114280 (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYen, E. \u0026amp; Maron, J. L. Aberrant Feeding and Growth in Neonates With Prenatal Opioid Exposure: Evidence of Neuromodulation and Behavioral Changes. \u003cem\u003eFront. Pediatr.\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 805763 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNugent, B. M., O\u0026rsquo;Donnell, C. M., Epperson, C. N. \u0026amp; Bale, T. L. Placental H3K27me3 establishes female resilience to prenatal insults. \u003cem\u003eNat. Commun.\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 2555 (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDiPietro, J. A. \u0026amp; Voegtline, K. M. The gestational foundation of sex differences in development and vulnerability. \u003cem\u003eNeuroscience\u003c/em\u003e\u003cstrong\u003e342\u003c/strong\u003e, 4\u0026ndash;20 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eE., J. H. \u003cem\u003eet al.\u003c/em\u003e Neonatal Abstinence Syndrome after Methadone or Buprenorphine Exposure. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e\u003cstrong\u003e363\u003c/strong\u003e, 2320\u0026ndash;2331 (2010).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHall, E. S. \u003cem\u003eet al.\u003c/em\u003e A Cohort Comparison of Buprenorphine versus Methadone Treatment for Neonatal Abstinence Syndrome. \u003cem\u003eJ. Pediatr.\u003c/em\u003e\u003cstrong\u003e170\u003c/strong\u003e, 39\u0026ndash;44.e1 (2016).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJones, H. E. \u003cem\u003eet al.\u003c/em\u003e Buprenorphine versus methadone in the treatment of pregnant opioid-dependent patients: effects on the neonatal abstinence syndrome. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e\u003cstrong\u003e79\u003c/strong\u003e, 1\u0026ndash;10 (2005).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGreen, J. M., Sundman, M. H. \u0026amp; Chou, Y. Opioid-induced microglia reactivity modulates opioid reward, analgesia, and behavior. \u003cem\u003eNeurosci. Biobehav. Rev.\u003c/em\u003e\u003cstrong\u003e135\u003c/strong\u003e, 104544 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSheikh, M. \u003cem\u003eet al.\u003c/em\u003e Opium use and subsequent incidence of cancer: results from the Golestan Cohort Study. \u003cem\u003eLancet Glob. Heal.\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, e649\u0026ndash;e660 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSun, M., Lin, J.-A., Chang, C.-L., Wu, S.-Y. \u0026amp; Zhang, J. Association between long-term opioid use and cancer risk in patients with chronic pain: a propensity score-matched cohort study. \u003cem\u003eBr. J. Anaesth.\u003c/em\u003e\u003cstrong\u003e129\u003c/strong\u003e, 84\u0026ndash;91 (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eShrestha, S., Foot, H., Sheikh, M., Parat, M.-O. \u0026amp; Caze, A. L. Opioid use and the risk of cancer incidence and mortality: a systematic review. \u003cem\u003eCancer Metastasis Rev.\u003c/em\u003e\u003cstrong\u003e44\u003c/strong\u003e, 54 (2025).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDietz, J. A., Johnson, K. L., Wick, H. C., Bianchi, D. W. \u0026amp; Maron, J. L. Optimal Techniques for mRNA Extraction from Neonatal Salivary Supernatant. \u003cem\u003eNeonatology\u003c/em\u003e\u003cstrong\u003e101\u003c/strong\u003e, 55 60 (2012).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYen, E., Kaneko-Tarui, T. \u0026amp; Maron, J. L. Technical Considerations and Protocol Optimization for Neonatal Salivary Biomarker Discovery and Analysis. \u003cem\u003eFrontiers Pediatrics\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 618553 (2021).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKhanna, P., Johnson, K. L. \u0026amp; Maron, J. L. Optimal reference genes for RT-qPCR normalization in the newborn. \u003cem\u003eBiotech Histochem\u003c/em\u003e\u003cstrong\u003e92\u003c/strong\u003e, 459 466 (2017).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"maternal, offspring, opioid exposure, sex differences, saliva, multiplex transcriptomics","lastPublishedDoi":"10.21203/rs.3.rs-7418166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7418166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOpioid use disorder affects males and females differently, yet the molecular mechanisms are understudied in neonates. Our laboratory has demonstrated differential sex effects of opioids on the reward and inflammatory pathways related to neonatal feeding behavior that may affect growth and cardiometabolic outcomes. This observational pilot study examined the sex-specific impact of maternal opioid use during pregnancy on reward, energy homeostasis, inflammation, oxidative stress, and neuropathology pathways in offspring. Saliva from nine opioid-exposed and nine non-exposed neonates collected within 48 hours after birth underwent a multiplex, high-throughput analysis of 72 select genes using NanoString\u0026rsquo;s nCounter\u0026reg; system (NanoString Technologies, Seattle, WA, USA. Despite low RNA abundance in neonatal saliva, experimental conditions were optimized after several trials. Multiplex analysis demonstrated sex-specific molecular effects of maternal opioid use, i.e., upregulated pathways related to reward, inflammation, oxidative stress, feeding, and energy homeostasis pathways in males, and downregulated pathways related to inflammation and cardiovascular function in females. This pilot study demonstrates the feasibility of multiplexing neonatal saliva using a high-throughput platform. Future work will replicate these methods and validate findings in a larger sample to clarify the sex-specific short and long-term impact of maternal opioid use on health outcomes.\u003c/p\u003e","manuscriptTitle":"A Pilot Multiplex Salivary Transcriptomic Analysis to Understand the Sex-specific Effects of Maternal Opioid Use in Offspring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 06:44:12","doi":"10.21203/rs.3.rs-7418166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T01:37:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T17:50:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T14:56:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214551795842355490291178693016799960130","date":"2025-09-24T11:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109365886027265289221459657796527531995","date":"2025-09-23T12:24:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7684602261489817961524855672425646133","date":"2025-09-20T14:13:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T22:25:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302542228763166932520042252568428560115","date":"2025-09-18T10:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68222999365758078939446289844552182688","date":"2025-09-18T00:24:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T08:46:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T08:17:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-12T02:57:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T04:27:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-08T18:22:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae9be443-1b74-40ae-a2ea-207f4ac4fe27","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55196980,"name":"Health sciences/Diseases"},{"id":55196981,"name":"Health sciences/Medical research"},{"id":55196982,"name":"Biological sciences/Neuroscience"},{"id":55196983,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-04-27T16:03:05+00:00","versionOfRecord":{"articleIdentity":"rs-7418166","link":"https://doi.org/10.1038/s41598-026-49873-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-24 15:57:27","publishedOnDateReadable":"April 24th, 2026"},"versionCreatedAt":"2025-09-24 06:44:12","video":"","vorDoi":"10.1038/s41598-026-49873-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-49873-6","workflowStages":[]},"version":"v1","identity":"rs-7418166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7418166","identity":"rs-7418166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}