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.

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.

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 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.

Haplotype analysis of ADAM33 polymorphisms in asthma: A pilot study.

Background & objectives: ADAM33 is implicated as a potentially strong candidate gene for asthma and bronchial hyper-responsiveness. Many polymorphisms of ADAM33 have been studied along with ADAM33 expression in various cells of the lungs. Haplotype analysis also showed association with asthma in different populations across the world. Therefore, the aim of this study was to perform a comprehensive screening of ADAM33 polymorphisms in adult patients with asthma. Methods: Thirty five polymorphisms of ADAM33 were genotyped in 55 patients with asthma and 53 controls. The association of single nucleotide polymorphisms (SNPs) and haplotypes with phenotypes of asthma was analysed. Results: The genotype, minor allele frequency, odds ratio and Hardy-Weinberg equilibrium did not show any significant difference among cases and controls. No association was found between SNPs of ADAM33 with the severity of asthma. Correlation analysis of ADAM33 SNPs to the phenotypes, based on clinical variables and allergen sensitization, did not show significant difference. Haplotype analysis showed that rs2280090 and rs2280091 were associated with asthma in the patient group. Interpretation & conclusions: Haplotype analysis showed an association of the two SNP variations with asthma. These SNPs lead to amino acid change and are prone to phosphorylation, which may affect expression levels and protein function of ADAM33 and asthma susceptibility.

Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy.

The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this improves long term outcomes. We performed genome-wide DNA methylation analysis to identify epigenomic predictors of CP in newborns and to investigate disease pathogenesis. Methylation analysis of newborn blood DNA using an Illumina HumanMethylation450K array was performed in 23 CP cases and 21 unaffected controls. There were 230 significantly differentially-methylated CpG loci in 258 genes. Each locus had at least 2.0-fold change in methylation in CP versus controls with a FDR p-value ≤ 0.05. Methylation level for each CpG locus had an area under the receiver operating curve (AUC) ≥ 0.75 for CP detection. Using Artificial Intelligence (AI) platforms/Machine Learning (ML) analysis, CpG methylation levels in a combination of 230 significantly differentially-methylated CpG loci in 258 genes had a 95% sensitivity and 94.4% specificity for newborn prediction of CP. Using pathway analysis, multiple canonical pathways plausibly linked to neuronal function were over-represented. Altered biological processes and functions included: neuromotor damage, malformation of major brain structures, brain growth, neuroprotection, neuronal development and de-differentiation, and cranial sensory neuron development. In conclusion, blood leucocyte epigenetic changes analyzed using AI/ML techniques appeared to accurately predict CP and provided plausible mechanistic information on CP pathogenesis.

A candidate gene identification strategy utilizing mouse to human big-data mining: "3R-tenet" in COPD genetic research.

BACKGROUND: Early life impairments leading to lower lung function by adulthood are considered as risk factors for chronic obstructive pulmonary disease (COPD). Recently, we compared the lung transcriptomic profile between two mouse strains with extreme total lung capacities to identify plausible pulmonary function determining genes using microarray analysis (GSE80078). Advancement of high-throughput techniques like deep sequencing (eg. RNA-seq) and microarray have resulted in an explosion of genomic data in the online public repositories which however remains under-exploited. Strategic curation of publicly available genomic data with a mouse-human translational approach can effectively implement "3R- Tenet" by reducing screening experiments with animals and performing mechanistic studies using physiologically relevant in vitro model systems. Therefore, we sought to analyze the association of functional variations within human orthologs of mouse lung function candidate genes in a publicly available COPD lung RNA-seq data-set. METHODS: Association of missense single nucleotide polymorphisms, insertions, deletions, and splice junction variants were analyzed for susceptibility to COPD using RNA-seq data of a Korean population (GSE57148). Expression of the associated genes were studied using the Gene Paint (mouse embryo) and Human Protein Atlas (normal adult human lung) databases. The genes were also assessed for replication of the associations and expression in COPD-/mouse cigarette smoke exposed lung tissues using other datasets. RESULTS: Significant association (p <  0.05) of variations in 20 genes to higher COPD susceptibility have been detected within the investigated cohort. Association of HJURP, MCRS1 and TLR8 are novel in relation to COPD. The associated ADAM19 and KIT loci have been reported earlier. The remaining 15 genes have also been previously associated to COPD. Differential transcript expression levels of the associated genes in COPD- and/ or mouse emphysematous lung tissues have been detected. CONCLUSION: Our findings suggest strategic mouse-human datamining approaches can identify novel COPD candidate genes using existing datasets in the online repositories. The candidates can be further evaluated for mechanistic role through in vitro studies using appropriate primary cells/cell lines. Functional studies can be limited to transgenic animal models of only well supported candidate genes. This approach will lead to a significant reduction of animal experimentation in respiratory research.

Transcriptomic analysis comparing mouse strains with extreme total lung capacities identifies novel candidate genes for pulmonary function.

BACKGROUND: Failure to attain peak lung function by early adulthood is a risk factor for chronic lung diseases. Previously, we reported that C3H/HeJ mice have about twice total lung capacity (TLC) compared to JF1/MsJ mice. We identified seven lung function quantitative trait loci (QTL: Lfnq1-Lfnq7) in backcross/intercross mice derived from these inbred strains. We further demonstrated, superoxide dismutase 3, extracellular (Sod3), Kit oncogene (Kit) and secreted phosphoprotein 1 (Spp1) located on these Lfnqs as lung function determinants. Emanating from the concept of early origin of lung disease, we sought to identify novel candidate genes for pulmonary function by investigating lung transcriptome in C3H/HeJ and JF1/MsJ mice at the completion of embryonic development, bulk alveolar formation and maturity. METHODS: Design-based stereological analysis was performed to study lung structure in C3H/HeJ and JF1/MsJ mice. Microarray was used for lung transcriptomic analysis [embryonic day 18, postnatal days 28, 70]. Quantitative real time polymerase chain reaction (qRT-PCR), western blot and immunohistochemical analysis were used to confirm selected differences. RESULTS: Stereological analysis revealed decreased alveolar number density, elastin to collagen ratio and increased mean alveolar volume in C3H/HeJ mice compared to JF1/MsJ. Gene ontology term "extracellular region" was enriched among the decreased JF1/MsJ transcripts. Candidate genes identified using the expression-QTL strategy include: ATP-binding cassette, sub-family G (WHITE), member 1 (Abcg1), formyl peptide receptor 1 (Fpr1), gamma-aminobutyric acid (GABA) B receptor, 1 (Gabbr1); histocompatibility 2 genes: class II antigen E beta (H2-Eb1), D region locus 1 (H2-D1), and Q region locus 4 (H2-Q4); leucine rich repeat containing 6 (testis) (Lrrc6), radial spoke head 1 homolog (Rsph1), and surfactant associated 2 (Sfta2). Noteworthy genes selected as candidates for their consistent expression include: Wnt inhibitor factor 1 (Wif1), follistatin (Fst), chitinase-like 1 (Chil1), and Chil3. CONCLUSIONS: Comparison of late embryonic, adolescent and adult lung transcript profiles between mouse strains with extreme TLCs lead to the identification of candidate genes for pulmonary function that has not been reported earlier. Further mechanistic investigations are warranted to elucidate their mode of action in determining lung function.

Global patterns of large copy number variations in the human genome reveal complexity in chromosome organization.

Global patterns of copy number variations (CNVs) in chromosomes are required to understand the dynamics of genome organization and complexity. For this study, analysis was performed using the Affymetrix Genome-Wide Human SNP Array 6.0 chip and CytoScan High-Density arrays. We identified a total of 44 109 CNVs from 1715 genomes with a mean of 25 CNVs in an individual, which established the first drafts of population-specific CNV maps providing a rationale for prioritizing chromosomal regions. About 19 905 ancient CNVs were identified across all chromosomes and populations at varying frequencies. CNV count, and sometimes CNV size, contributed to the bulk CNV size of the chromosome. Population specific lengthening and shortening of chromosomal length was observed. Sex bias for CNV presence was largely dependent on ethnicity. Lower CNV inheritance rate was observed for India, compared to YRI and CEU. A total of 33 candidate CNV hotspots from 5382 copy number (CN) variable region (CNVR) clusters were identified. Population specific CNV distribution patterns in p and q arms disturbed the assumption that CNV counts in the p arm are less common compared to long arms, and the CNV occurrence and distribution in chromosomes is length independent. This study unraveled the force of independent evolutionary dynamics on genome organization and complexity across chromosomes and populations.

IL-10 and IL-17F Promoter Single Nucleotide Polymorphism and Asthma: A Case-Control Study in South India.

BACKGROUND: Several studies have assessed the association between IL-17F and IL-10 promoter polymorphisms and asthma, but the results were conflicting. Furthermore, few studies have evaluated the association of cytokine polymorphisms with asthma and its clinical phenotypes. OBJECTIVE: This study was conducted to evaluate the association of IL-10 (interleukin 10) and IL-17F (interleukin 17F) promoter polymorphisms (rs1800871, rs1800896 and rs1889570) with asthma and its clinical phenotypes including severity, atopic status, spirometric parameters, and response to treatment in south Indian population. A sub-study was conducted to assess cytokine levels in subjects with different gene variants. METHODS: IL-10 and IL-17F polymorphisms were genotyped in 419 asthmatic patients and 393 controls using Mass ARRAY. RESULTS: Our results showed an association between IL-10 SNPs and mild asthma. No association was found with any of three SNPs in moderate to severe asthma. Comparison of genotype distribution of IL-17F rs1887570 AA variant among atopic and non-atopic patients showed significant difference (p = 0.024). Correlation analysis of IL-10 and IL-17F SNPs to clinical variables showed a positive correlation between IL-17F rs1887570 AA and number of allergen sensitized (rs = 0.142, p = 0.004). Significant improvement in lung function was observed after 2 months of ICS (Inhaled corticosteroids) and LABA (long acting ß2 agonist) treatment in all subjects with no statistically significant difference among SNPs variants. Cytokines levels were similar in different SNP variants. CONCLUSION: We observed an association between IL-10 rs1800871 and rs1800896 SNPs and mild asthma, as well as IL-17F rs1887570 AA variant and number of allergens sensitized.

Impact of copy number variations burden on coding genome in humans using integrated high resolution arrays.

Copy number variations (CNVs) alter the transcriptional and translational levels of genes by disrupting the coding structure and this burden of CNVs seems to be a significant contributor to phenotypic variations. Therefore it was necessary to assess the complexities of CNV burden on the coding genome. A total of 1715 individuals from 12 populations were used for CNV analysis in the present investigation. Analysis was performed using Affymetrix Genome-Wide Human SNP Array 6·0 chip and CytoScan High-Density arrays. CNVs were more frequently observed in the coding region than in the non-coding region. CNVs were observed vastly more frequently in the coding region than the non-coding region. CNVs were found to be enriched in the regions containing functional genes (83-96%) compared with the regions containing pseudogenes (4-17%). CNVs across the genome of an individual showed multiple hits across many genes, whose proteins interact physically and function under the same pathway. We identified varying numbers of proteins and degrees of interactions within protein complexes of single individual genomes. This study represents the first draft of a population-specific CNV genes map as well as a cross-populational map. The complex relationship of CNVs on genes and their physically interacting partners unravels many complexities involved in phenotype expression. This study identifies four mechanisms contributing to the complexities caused by the presence of multiple CNVs across many genes in the coding part of the genome.

Molecular interaction network and pathway studies of ADAM33 potentially relevant to asthma.

BACKGROUND: Asthma is a complex disease caused by gene-gene, gene-protein, and protein-protein interactions and the influence of environment, which plays a significant role in causing asthma pathogenesis. ADAM33 is known to be an important gene involved in asthma pathogenesis. No one single gene is a causal factor of asthma; rather, asthma is caused by a complex interaction of multiple genes having pathogenetic and protective effects. OBJECTIVE: To identify and understand the interacting genes and proteins of ADAM33. METHODS: The Ingenuity Pathway Analysis and GeneMANIA tools and a literature survey were used to identify the interacting candidates of ADAM33 and the WEB-based GEne SeT AnaLysis Toolkit was used to perform enrichment analysis of the proteins identified. RESULTS: Keeping ADAM33 as a major hub, the authors identified some proteins whose interaction with ADAM33 had been associated with asthma and they recognized some proteins, such as amyloid ß (A4) precursor protein, ataxin-7, α4-integrin, α5-integrin, α9-integrin, tissue inhibitor of metalloproteinase-4, and ubiquilin-4, that had not been previously associated with asthma. CONCLUSION: The proteins identified in this study were enriched for various mechanisms that are involved in airway hyperresponsiveness, and through the interaction with ADAM33, they may have potential relevance in asthma.

Serum levels of IL-10, IL-17F and IL-33 in patients with asthma: a case-control study.

OBJECTIVES: The development of inflammation in asthma involves an intricate network of cytokines that recruit and activate numerous immune cells. This study was aimed to compare serum levels of IL-10, IL-17F, and IL-33 in asthmatic patients and non-asthmatic controls and correlate cytokine levels to asthma severity and various clinical, spirometric, and laboratory variables. METHODS: Using ELISA, serum levels of IL-10, IL-17F, and IL-33 were evaluated in 44 asthmatics (14 mild persistent, 15 moderate persistent, and 15 severe persistent) and 44 controls. RESULTS: This is one of the first reports showing a significant difference in serum levels of asthma-associated cytokines, anti-inflammatory IL-10, and pro-inflammatory IL-17F and IL-33, in the same subset of asthmatic patients. Our results showed diminished level of IL-10 and elevated levels of IL-17F and IL-33 in asthmatics than in controls (p < 0.001). Assessment of cytokine levels between subjects of different gender, age group, and BMI showed non-significant differences. Correlation analysis of cytokine levels to clinical variables showed that IL-17F is associated negatively to FVC % predicted (forced vital capacity) and FEV1% predicted (forced expiratory volume in one second) and positively to number of allergens sensitized and FEV1 reversibility. A strong negative correlation was found between IL-10 and IL-33 levels (p = 0.001). CONCLUSIONS: Negative correlation between IL-10 and IL-33 levels may reflect a converse relationship between anti-inflammatory and pro-inflammatory cytokines in an individually balanced pattern. The association between IL-17F level and asthmatic phenotypes such as reduced FVC and FEV1, higher degree of sensitization, and post-bronchodilator reversibility needs further assessments.

Insertion-deletions burden in copy number polymorphisms of the Tibetan population.

BACKGROUND: Many studies have been conducted to identify either insertions-deletions (inDels) or copy number variations (CNVs) in humans, but few studies have been conducted to identify both of these forms coexisting in the same region. AIMS AND OBJECTIVES: To map the functionally significant sites within human genes that are likely to influence human traits and diseases. MATERIALS AND METHODS: In this report, we describe an inDel map in the 1051 Tibetan CNV regions obtained through CNV genotyping using Affymetrix Genome-wide single nucleotide polymorphism 6.0 chip. InDel polymorphisms in these copy number polymorphism regions were identified with a computational approach using the 2500 deoxyribonucleic acid sequences obtained from the 1000 Genome Project. RESULTS: The study identified a total of 95935 inDels that range from 1 bp to several bps in length which were found scattered across regulatory regions, exons and in introns of genes underlying the CNVs. A study on the distribution of inDels revealed that the majority of inDels were found in coding regions of the genome than the noncoding, while within the genes, inDels in intron regions were more followed by exonic regions and finally the regulatory regions. CONCLUSION: Study of inDels in CNV regions contribute to the enhanced understanding of the role played by the two variations and their collective influence on the genome. Further, a collection of these inDel genetic markers will aid in genetic mapping, further understanding of the phenotypic variability, identification of disease genes and in detecting novel CNVs.

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.

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.

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.

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 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.

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.