Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study.

BACKGROUND: Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC. METHODS: The Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis. RESULTS: In total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed. CONCLUSION: Using a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington's Disease Brain Tissue.

The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.

Placental microRNA methylome signatures may serve as biomarkers and therapeutic targets for prenatally opioid-exposed infants with neonatal opioid withdrawal syndrome.

Introduction: The neonate exposed to opioids in utero faces a constellation of withdrawal symptoms postpartum commonly called neonatal opioid withdrawal syndrome (NOWS). The incidence of NOWS has increased in recent years due to the opioid epidemic. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in gene regulation. Epigenetic variations in microRNAs (miRNAs) and their impact on addiction-related processes is a rapidly evolving area of research. Methods: The Illumina Infinium Methylation EPIC BeadChip was used to analyze DNA methylation levels of miRNA-encoding genes in 96 human placental tissues to identify miRNA gene methylation profiles as-sociated with NOWS: 32 from mothers whose prenatally opioid-exposed infants required pharmacologic management for NOWS, 32 from mothers whose prenatally opioid-exposed infants did not require treat-ment for NOWS, and 32 unexposed controls. Results: The study identified 46 significantly differentially methylated (FDR p-value ≤ 0.05) CpGs associated with 47 unique miRNAs, with a receiver operating characteristic (ROC) area under the curve (AUC) ≥0.75 including 28 hypomethylated and 18 hypermethylated CpGs as potentially associated with NOWS. These dysregulated microRNA methylation patterns may be a contributing factor to NOWS pathogenesis. Conclusion: This is the first study to analyze miRNA methylation profiles in NOWS infants and illustrates the unique role miRNAs might have in diagnosing and treating the disease. Furthermore, these data may provide a step toward feasible precision medicine for NOWS babies as well.

Alzheimer's Precision Neurology: Epigenetics of Cytochrome P450 Genes in Circulating Cell-Free DNA for Disease Prediction and Mechanism.

Precision neurology combines high-throughput technologies and statistical modeling to identify novel disease pathways and predictive biomarkers in Alzheimer's disease (AD). Brain cytochrome P450 (CYP) genes are major regulators of cholesterol, sex hormone, and xenobiotic metabolism, and they could play important roles in neurodegenerative disorders. Increasing evidence suggests that epigenetic factors contribute to AD development. We evaluated cytosine ('CpG')-based DNA methylation changes in AD using circulating cell-free DNA (cfDNA), to which neuronal cells are known to contribute. We investigated CYP-based mechanisms for AD pathogenesis and epigenetic biomarkers for disease detection. We performed a case-control study using 25 patients with AD and 23 cognitively healthy controls using the cfDNA of CYP genes. We performed a logistic regression analysis using the MetaboAnalyst software computer program and a molecular pathway analysis based on epigenetically altered CYP genes using the Cytoscape program. We identified 130 significantly (false discovery rate correction q-value < 0.05) differentially methylated CpG sites within the CYP genes. The top two differentially methylated genes identified were CYP51A1 and CYP2S1. The significant molecular pathways that were perturbed in AD cfDNA were (i) androgen and estrogen biosynthesis and metabolism, (ii) C21 steroid hormone biosynthesis and metabolism, and (iii) arachidonic acid metabolism. Existing evidence suggests a potential role of each of these biochemical pathways in AD pathogenesis. Next, we randomly divided the study group into discovery and validation sub-sets, each consisting of patients with AD and control patients. Regression models for AD prediction based on CYP CpG methylation markers were developed in the discovery or training group and tested in the independent validation group. The CYP biomarkers achieved a high predictive accuracy. After a 10-fold cross-validation, the combination of cg17852385/cg23101118 + cg14355428/cg22536554 achieved an AUC (95% CI) of 0.928 (0.787~1.00), with 100% sensitivity and 92.3% specificity for AD detection in the discovery group. The performance remained high in the independent validation or test group, achieving an AUC (95% CI) of 0.942 (0.905~0.979) with a 90% sensitivity and specificity. Our findings suggest that the epigenetic modification of CYP genes may play an important role in AD pathogenesis and that circulating CYP-based cfDNA biomarkers have the potential to accurately and non-invasively detect AD.

Association between ADAM33 Single-Nucleotide Polymorphisms and Treatment Response to Inhaled Corticosteroids and a Long-Acting Beta-Agonist in Asthma.

ADAM33 has been linked to airway structural changes in patients with asthma, leading to airway hyperresponsiveness, narrowing, and ultimately poor treatment responsiveness. This study aimed to evaluate the genetic association of ADAM33 SNPs with asthma, disease severity, and treatment responsiveness to ICS+LABA in the South Indian population. In this case-control study (486 controls and 503 cases), we performed genotyping using MassArray for six SNPs of ADAM33, namely rs2280091, rs2787094, rs3918396, rs67044, rs2853209, and rs3918392. We studied the association with asthma and treatment responsiveness to ICS+LABA, using genotype, allele frequency distribution, and haplotype analysis. A significant clinical finding of the study was that certain patients in the disease severity group (moderate and mild) showed poor or no improvement after a three-month follow-up of regular ICS+LABA therapy. Of the studied ADAM33 SNPs, rs2853209 showed an association with asthma. The further analysis of asthma patients according to disease severity suggested an association between moderate disease and the minor allele "T" for rs2853209. The homozygous minor allele of SNP rs2787094 was found to be associated with poorer lung function and the least lung-function improvement after three months of ICS+LABA therapy. The haplotype analysis of six SNPs showed a significant association between the rs2853209 and rs3918396 blocks and asthma. ADAM33 gene polymorphism has clinical relevance in terms of disease association and response to treatment. SNP rs2853209 seemed most relevant to asthma, and SNP rs2787094 could be a genetic marker for predicting response to ICS+LABA therapy in the study population.

Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects.

BACKGROUND: DNA cytosine nucleotide methylation (epigenomics and epigenetics) is an important mechanism for controlling gene expression in cardiac development. Combined artificial intelligence and whole-genome epigenomic analysis of circulating cell-free DNA in maternal blood has the potential for the detection of fetal congenital heart defects. OBJECTIVE: This study aimed to use genome-wide DNA cytosine methylation and artificial intelligence analyses of circulating cell-free DNA for the minimally invasive detection of fetal congenital heart defects. STUDY DESIGN: In this prospective study, whole-genome cytosine nucleotide methylation analysis was performed on circulating cell-free DNA using the Illumina Infinium MethylationEPIC BeadChip array. Multiple artificial intelligence approaches were evaluated for the detection of congenital hearts. The Ingenuity Pathway Analysis program was used to identify gene pathways that were epigenetically altered and important in congenital heart defect pathogenesis to further elucidate the pathogenesis of isolated congenital heart defects. RESULTS: There were 12 cases of isolated nonsyndromic congenital heart defects and 26 matched controls. A total of 5918 cytosine nucleotides involving 4976 genes had significantly altered methylation, that is, a P value of <.05 along with ≥5% whole-genome cytosine nucleotide methylation difference, in congenital heart defect cases vs controls. Artificial intelligence analysis of the methylation data achieved excellent congenital heart defect predictive accuracy (areas under the receiver operating characteristic curve, ≥0.92). For example, an artificial intelligence model using a combination of 5 whole-genome cytosine nucleotide markers achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.87-1.0) with 98% sensitivity and 94% specificity. We found epigenetic changes in genes and gene pathways involved in the following important cardiac developmental processes: "cardiovascular system development and function," "cardiac hypertrophy," "congenital heart anomaly," and "cardiovascular disease." This lends biologic plausibility to our findings. CONCLUSION: This study reported the feasibility of minimally invasive detection of fetal congenital heart defect using artificial intelligence and DNA methylation analysis of circulating cell-free DNA for the prediction of fetal congenital heart defect. Furthermore, the findings supported an important role of epigenetic changes in congenital heart defect development.

Placental cytochrome P450 methylomes in infants exposed to prenatal opioids: exploring the effects of neonatal opioid withdrawal syndrome on health horizons.

Background: Neonatal opioid withdrawal syndrome (NOWS), arises due to increased opioid use during pregnancy. Cytochrome P450 (CYP) enzymes play a pivotal role in metabolizing a wide range of substances in the human body, including opioids, other drugs, toxins, and endogenous compounds. The association between CYP gene methylation and opioid effects is unexplored and it could offer promising insights. Objective: To investigate the impact of prenatal opioid exposure on disrupted CYPs in infants and their anticipated long-term clinical implications. Study Design: DNA methylation levels of CYP genes were analyzed in a cohort of 96 placental tissues using Illumina Infinium MethylationEPIC (850 k) BeadChips. This involved three groups of placental tissues: 32 from mothers with infants exposed to opioids prenatally requiring pharmacologic treatment for NOWS, 32 from mothers with prenatally opioid-exposed infants not needing NOWS treatment, and 32 from unexposed control mothers. Results: The study identified 20 significantly differentially methylated CpG sites associated with 17 distinct CYP genes, with 14 CpGs showing reduced methylation across 14 genes (CYP19A1, CYP1A2, CYP4V2, CYP1B1, CYP24A1, CYP26B1, CYP26C1, CYP2C18, CYP2C9, CYP2U1, CYP39A1, CYP2R1, CYP4Z1, CYP2D7P1 and), while 8 exhibited hypermethylation (CYP51A1, CYP26B1, CYP2R1, CYP2U1, CYP4X1, CYP1A2, CYP2W1, and CYP4V2). Genes such as CYP1A2, CYP26B1, CYP2R1, CYP2U1, and CYP4V2 exhibited both increased and decreased methylation. These genes are crucial for metabolizing eicosanoids, fatty acids, drugs, and diverse substances. Conclusion: The study identified profound methylation changes in multiple CYP genes in the placental tissues relevant to NOWS. This suggests that disruption of DNA methylation patterns in CYP transcripts might play a role in NOWS and may serve as valuable biomarkers, suggesting a future pathway for personalized treatment. Further research is needed to confirm these findings and explore their potential for diagnosis and treatment.

Dementia with Lewy bodies post-mortem brains reveal differentially methylated CpG sites with biomarker potential.

Dementia with Lewy bodies (DLB) is a common form of dementia with known genetic and environmental interactions. However, the underlying epigenetic mechanisms which reflect these gene-environment interactions are poorly studied. Herein, we measure genome-wide DNA methylation profiles of post-mortem brain tissue (Broadmann area 7) from 15 pathologically confirmed DLB brains and compare them with 16 cognitively normal controls using Illumina MethylationEPIC arrays. We identify 17 significantly differentially methylated CpGs (DMCs) and 17 differentially methylated regions (DMRs) between the groups. The DMCs are mainly located at the CpG islands, promoter and first exon regions. Genes associated with the DMCs are linked to "Parkinson's disease" and "metabolic pathway", as well as the diseases of "severe intellectual disability" and "mood disorders". Overall, our study highlights previously unreported DMCs offering insights into DLB pathogenesis with the possibility that some of these could be used as biomarkers of DLB in the future.

Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer.

Ovarian cancer (OC) is the most lethal gynecologic cancer due primarily to its asymptomatic nature and late stage at diagnosis. The development of non-invasive markers is an urgent priority. We report the accurate detection of epithelial OC using Artificial Intelligence (AI) and genome-wide epigenetic analysis of circulating cell free tumor DNA (cfTDNA). In a prospective study, we performed genome-wide DNA methylation profiling of cytosine (CpG) markers. Both conventional logistic regression and six AI platforms were used for OC detection. Further, we performed Gene Set Enrichment Analysis (GSEA) analysis to elucidate the molecular pathogenesis of OC. A total of 179,238 CpGs were significantly differentially methylated (FDR p-value < 0.05) genome-wide in OC. High OC diagnostic accuracies were achieved. Conventional logistic regression achieved an area under the ROC curve (AUC) [95% CI] 0.99 [± 0.1] with 95% sensitivity and 100% specificity. Multiple AI platforms each achieved high diagnostic accuracies (AUC = 0.99-1.00). For example, for Deep Learning (DL)/AI AUC = 1.00, sensitivity = 100% and 88% specificity. In terms of OC pathogenesis: GSEA analysis identified 'Adipogenesis' and 'retinoblastoma gene in cancer' as the top perturbed molecular pathway in OC. This finding of epigenomic dysregulation of molecular pathways that have been previously linked to cancer adds biological plausibility to our results.

Decreased Levels of DNA Methylation in the PCDHA Gene Cluster as a Risk Factor for Early-Onset High Myopia in Young Children.

Purpose: High myopia (HM), an eye disorder with at least -6.0 diopters refractive error, has a complex etiology with environmental, genetic, and likely epigenetic factors involved. To complement the DNA methylation assessment in children with HM, we analyzed genes that had significantly lower DNA methylation levels. Methods: The DNA methylation pattern was studied based on the genome-wide methylation data of 18 Polish children with HM paired with 18 controls. Genes overlapping CG dinucleotides with decreased methylation level in HM cases were assessed by enrichment analyses. From those, genes with CG dinucleotides in promoter regions were further evaluated based on exome sequencing (ES) data of 16 patients with HM from unrelated Polish families, Sanger sequencing data of the studied children, and the RNA sequencing data of human retinal ARPE-19 cells. Results: The CG dinucleotide with the most decreased methylation level in cases was identified in a promoter region of PCDHA10 that overlaps intronic regions of PCDHA1-9 of the PCDHA gene cluster in myopia 5q31 locus. Also, two single nucleotide variants, rs200661444, detected in our ES, and rs246073, previously found as associated with a refractive error in a genome-wide association study, were revealed within this gene cluster. Additionally, genes previously linked to ocular phenotypes, myopia-related traits, or loci, including ADAM20, ZFAND6, ETS1, ABHD13, SBSPON, SORBS2, LMOD3, ATXN1, and FARP2, were found to have decreased methylation. Conclusions: Alterations in the methylation pattern of specific CG dinucleotides may be associated with early-onset HM, so this could be used to develop noninvasive biomarkers of HM in children and adolescents.

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease.

Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer's disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.

Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection.

Background: Lung cancer (LC) is a leading cause of cancer-deaths globally. Its lethality is due in large part to the paucity of accurate screening markers. Precision Medicine includes the use of omics technology and novel analytic approaches for biomarker development. We combined Artificial Intelligence (AI) and DNA methylation analysis of circulating cell-free tumor DNA (ctDNA), to identify putative biomarkers for and to elucidate the pathogenesis of LC. Methods: Illumina Infinium MethylationEPIC BeadChip array analysis was used to measure cytosine (CpG) methylation changes across the genome in LC. Six different AI platforms including support vector machine (SVM) and Deep Learning (DL) were used to identify CpG biomarkers and for LC detection. Training set and validation sets were generated, and 10-fold cross validation performed. Gene enrichment analysis using g:profiler and GREAT enrichment was used to elucidate the LC pathogenesis. Results: Using a stringent GWAS significance threshold, p-value <5x10-8, we identified 4389 CpGs (cytosine methylation loci) in coding genes and 1812 CpGs in non-protein coding DNA regions that were differentially methylated in LC. SVM and three other AI platforms achieved an AUC=1.00; 95% CI (0.90-1.00) for LC detection. DL achieved an AUC=1.00; 95% CI (0.95-1.00) and 100% sensitivity and specificity. High diagnostic accuracies were achieved with only intragenic or only intergenic CpG loci. Gene enrichment analysis found dysregulation of molecular pathways involved in the development of small cell and non-small cell LC. Conclusion: Using AI and DNA methylation analysis of ctDNA, high LC detection rates were achieved. Further, many of the genes that were epigenetically altered are known to be involved in the biology of neoplasms in general and lung cancer in particular.

Methylated Cytochrome P450 and the Solute Carrier Family of Genes Correlate With Perturbations in Bile Acid Metabolism in Parkinson's Disease.

Parkinson's disease (PD) is second most prevalent neurodegenerative disorder following Alzheimer's disease. Parkinson's disease is hypothesized to be caused by a multifaceted interplay between genetic and environmental factors. Herein, and for the first time, we describe the integration of metabolomics and epigenetics (genome-wide DNA methylation; epimetabolomics) to profile the frontal lobe from people who died from PD and compared them with age-, and sex-matched controls. We identified 48 metabolites to be at significantly different concentrations (FDR q < 0.05), 4,313 differentially methylated sites [5'-C-phosphate-G-3' (CpGs)] (FDR q < 0.05) and increased DNA methylation age in the primary motor cortex of people who died from PD. We identified Primary bile acid biosynthesis as the major biochemical pathway to be perturbed in the frontal lobe of PD sufferers, and the metabolite taurine (p-value = 5.91E-06) as being positively correlated with CpG cg14286187 (SLC25A27; CYP39A1) (FDR q = 0.002), highlighting previously unreported biochemical changes associated with PD pathogenesis. In this novel multi-omics study, we identify regulatory mechanisms which we believe warrant future translational investigation and central biomarkers of PD which require further validation in more accessible biomatrices.

Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children.

BACKGROUND: Autism Spectrum Disorder (ASD) represents a heterogeneous group of disorders with a complex genetic and epigenomic etiology. DNA methylation is the most extensively studied epigenomic mechanism and correlates with altered gene expression. Artificial intelligence (AI) is a powerful tool for group segregation and for handling the large volume of data generated in omics experiments. METHODS: We performed genome-wide methylation analysis for differential methylation of cytosine nucleotide (CpG) was performed in 20 postpartum placental tissue samples from preterm births. Ten newborns went on to develop autism (Autistic Disorder subtype) and there were 10 unaffected controls. AI including Deep Learning (AI-DL) platforms were used to identify and rank cytosine methylation markers for ASD detection. Ingenuity Pathway Analysis (IPA) to identify genes and molecular pathways that were dysregulated in autism. RESULTS: We identified 4870 CpG loci comprising 2868 genes that were significantly differentially methylated in ASD compared to controls. Of these 431 CpGs met the stringent EWAS threshold (p-value <5 × 10-8) along with ≥10% methylation difference between CpGs in cases and controls. DL accurately predicted autism with an AUC (95% CI) of 1.00 (1-1) and sensitivity and specificity of 100% using a combination of 5 CpGs [cg13858611 (NRN1), cg09228833 (ZNF217), cg06179765 (GPNMB), cg08814105 (NKX2-5), cg27092191 (ZNF267)] CpG markers. IPA identified five prenatally dysregulated molecular pathways linked to ASD. CONCLUSIONS: The present study provides substantial evidence that epigenetic differences in placental tissue are associated with autism development and raises the prospect of early and accurate detection of the disorder.

New-onset postpartum preeclampsia: epigenetic mechanism and prediction.

OBJECTIVE: Placental cytosine (CpG) methylation was measured to predict new-onset postpartum preeclampsia (NOPP) and interrogate its molecular pathogenesis. METHODS: NOPP was defined as patients with a new diagnosis of postpartum preeclampsia developing ≥48 h to ≤6 weeks after delivery with no prior hypertensive disorders. Placental tissue was obtained from 12 NOPP cases and 12 normotensive controls. Genome-wide individual cytosine (CpG) methylation level was measured with the Infinium MethylationEPIC BeadChip array. Significant differential methylation (NOPP vs. controls) for individual CpG loci was defined as false discovery rate (FDR) p value <.05. Gene functional enrichment using Qiagen's ingenuity pathway analysis (IPA) was performed to help elucidate the molecular pathogenesis of NOPP. A logistic regression model for NOPP prediction based on the methylation level in a combination of CpG loci was generated. The area under the receiver operating characteristic curves (AUC [95% CI]) sensitivity, and specificity for NOPP prediction based on the CpG methylation level was calculated for each locus. RESULTS: There were 537 (in 540 separate genes) significantly (FDR p<.05 with a ≥ 2.0-fold methylation difference) differentially methylated CpG loci between the groups. A total of 143 individual CpG markers had excellent individual predictive accuracy for NOPP prediction (AUC ≥0.80), of which 14 markers had outstanding accuracy (AUC ≥0.90). A logistic regression model based on five CpG markers yielded an AUC (95% CI)=0.99 (0.95-0.99) with sensitivity 95% and specificity 93% for NOPP prediction. IPA revealed dysregulation of critical pathways (e.g., angiogenesis, chronic inflammation, and epithelial-mesenchymal transition) known to be linked to classic preeclampsia, in addition to other previously undescribed genes/pathways. CONCLUSIONS: There was significant placental epigenetic dysregulation in NOPP. NOPP shared both common and unique molecular pathways with classic preeclampsia. Finally, we have identified novel potential biomarkers for the early post-partum prediction of NOPP.

Precision cardiovascular medicine: artificial intelligence and epigenetics for the pathogenesis and prediction of coarctation in neonates.

BACKGROUND: Advances in omics and computational Artificial Intelligence (AI) have been said to be key to meeting the objectives of precision cardiovascular medicine. The focus of precision medicine includes a better assessment of disease risk and understanding of disease mechanisms. Our objective was to determine whether significant epigenetic changes occur in isolated, non-syndromic CoA. Further, we evaluated the AI analysis of DNA methylation for the prediction of CoA. METHODS: Genome-wide DNA methylation analysis of newborn blood DNA was performed in 24 isolated, non-syndromic CoA cases and 16 controls using the Illumina HumanMethylation450 BeadChip arrays. Cytosine nucleotide (CpG) methylation changes in CoA in each of 450,000 CpG loci were determined. Ingenuity pathway analysis (IPA) was performed to identify molecular and disease pathways that were epigenetically dysregulated. Using methylation data, six artificial intelligence (AI) platforms including deep learning (DL) was used for CoA detection. RESULTS: We identified significant (FDR p-value ≤ .05) methylation changes in 65 different CpG sites located in 75 genes in CoA subjects. DL achieved an AUC (95% CI) = 0.97 (0.80-1) with 95% sensitivity and 98% specificity. Gene ontology (GO) analysis yielded epigenetic alterations in important cardiovascular developmental genes and biological processes: abnormal morphology of cardiovascular system, left ventricular dysfunction, heart conduction disorder, thrombus formation, and coronary artery disease. CONCLUSION: In an exploratory study we report the use of AI and epigenomics to achieve important objectives of precision cardiovascular medicine. Accurate prediction of CoA was achieved using a newborn blood spot. Further, we provided evidence of a significant epigenetic etiology in isolated CoA development.

Differential methylation of microRNA encoding genes may contribute to high myopia.

Introduction: High myopia (HM), an eye disorder with a refractive error ≤-6.0 diopters, has multifactorial etiology with environmental and genetic factors involved. Recent studies confirm the impact of alterations in DNA methylation and microRNAs (miRNAs) on myopia. Here, we studied the combined aspects evaluating to the role of methylation of miRNA encoding genes in HM. Materials and Methods: From the genome-wide DNA methylation data of 18 Polish children with HM and 18 matched controls, we retrieved differentially methylated CG dinucleotides localized in miRNA encoding genes. Putative target genes of the highest-ranked miRNAs were obtained from the miRDB and included in overrepresentation analyses in the ConsensusPathDB. Expression of target genes was assessed using the RNA sequencing data of retinal ARPE-19 cell line. Results: We identified differential methylation of CG dinucleotides in promoter regions of MIR3621, MIR34C, MIR423 (increased methylation level), and MIR1178, MIRLET7A2, MIR885, MIR548I3, MIR6854, MIR675, MIRLET7C, MIR99A (decreased methylation level) genes. Several targets of these miRNAs, e.g. GNAS, TRAM1, CTNNB1, EIF4B, TENM3 and RUNX were previously associated with myopia/HM/refractive error in Europeans in genome-wide association studies. Overrepresentation analyses of miRNAs' targets revealed enrichment in pathways/processes related to eye structure/function, such as axon guidance, transcription, focal adhesion, and signaling pathways of TGF-ß, insulin, MAPK and EGF-EGFR. Conclusion: Differential methylation of indicated miRNA encoding genes might influence their expression and contribute to HM pathogenesis via disrupted regulation of transcription of miRNAs' target genes. Methylation of genes encoding miRNAs may be a new direction in research on both the mechanisms determining HM and non-invasive indicators in diagnostics.

Maternal opioid use disorder: Placental transcriptome analysis for neonatal opioid withdrawal syndrome.

Excessive prenatal opioid exposure may lead to the development of Neonatal Opioid Withdrawal Syndrome (NOWS). RNA-seq was done on 64 formalin-fixed paraffin-embedded placental tissue samples from 32 mothers with opioid use disorder, with newborns with NOWS that required treatment, and 32 prenatally unexposed controls. We identified 93 differentially expressed genes in the placentas of infants with NOWS compared to unexposed controls. There were 4 up- and 89 downregulated genes. Among these, 7 genes CYP1A1, APOB, RPH3A, NRXN1, LINC01206, AL157396.1, UNC80 achieved an FDR p-value of <0.01. The remaining 87 genes were significant with FDR p-value <0.05. The 4 upregulated, CYP1A1, FP671120.3, RAD1, RN7SL856P, and the 10 most significantly downregulated genes were RNA5SP364, GRIN2A, UNC5D, DMBT1P1, MIR3976HG, LINC02199, LINC02822, PANTR1, AC012178.1, CTNNA2. Ingenuity Pathway Analysis identified the 7 most likely to play an important role in the etiology of NOWS. Our study expands insights into the genetic mechanisms of NOWS development.

Placental DNA methylation changes and the early prediction of autism in full-term newborns.

Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00-1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.

Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome.

Opioid abuse during pregnancy can result in Neonatal Opioid Withdrawal Syndrome (NOWS). We investigated genome-wide methylation analyses of 96 placental tissue samples, including 32 prenatally opioid-exposed infants with NOWS who needed therapy (+Opioids/+NOWS), 32 prenatally opioid-exposed infants with NOWS who did not require treatment (+Opioids/-NOWS), and 32 prenatally unexposed controls (-Opioids/-NOWS, control). Statistics, bioinformatics, Artificial Intelligence (AI), including Deep Learning (DL), and Ingenuity Pathway Analyses (IPA) were performed. We identified 17 dysregulated pathways thought to be important in the pathophysiology of NOWS and reported accurate AI prediction of NOWS diagnoses. The DL had an AUC (95% CI) =0.98 (0.95-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS from the +Opioids/-NOWS group and AUCs (95% CI) =1.00 (1.0-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS versus control and + Opioids/-NOWS group versus controls. This study provides strong evidence of methylation dysregulation of placental tissue in NOWS development.