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.

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.

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-β, 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.

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.

Methylated miRNAs may serve as potential biomarkers and therapeutic targets for hidradenitis suppurativa

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic inflammatory disease influenced by genetics, non-genetic, and environmental factors that modulate miRNA expression. Currently, no miRNA data are available for HS. In this study, we profiled DNA methylation patterns of miRNA genes associated with HS susceptibility.

OBJECTIVES

Identify miRNA gene methylation profiles associated with HS susceptibility. This study examined the methylation patterns of DNAs from 24 healthy controls and 24 patients with HS using Illumina Infinium MethylationEPIC BeadChip array analysis. methylation patterns of miRNA genes were analyzed using Ingenuity Pathway Analysis (IPA) to explore the inversely correlated pathways regulated by miRNAs.

RESULTS

We identified 60 CpG sites representing 65 unique microRNA genes including 54 hypomethylated and 6 hypermethylated CpGs as potentially associated with HS. Some of these CpGs were found to be critical for skin function, such as miR-29, miR-200, miR-205, miR-548, and miR-132. The miR-192 is implicated in non-alcoholic fatty liver disease. The miR-200c gene was identified as a vital determinant in regulating skin repair after injury and may contribute to age-associated alterations in wound repair. miR-132 was significantly upregulated during the inflammation phase of wound repair, enhancing the activity of STAT3 and ERK pathways that promote keratinocyte proliferation.

CONCLUSIONS

Epigenetically altered microRNA genes are implicated in wound healing, inflammation, keratinocyte proliferation, and wound modulation. This is the first study to analyze methylation profiles of miRNA genes in the HS population, highlighting the unique role that miRNAs might play in diagnosing and treating HS.

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.

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.

Placental DNA methylation changes in detection of tetralogy of Fallot

OBJECTIVES

To determine whether the methylation level of cytosine nucleotides in placental DNA can be used to predict tetralogy of Fallot (TOF) and provide insights into the developmental mechanism of this condition.

METHODS

Tissue sections were obtained from formalin-fixed paraffin-embedded specimens of placental tissue obtained at birth from eight cases with non-chromosomal, non-syndromic TOF and 10 unaffected newborns. The Illumina Infinium HumanMethylation450 BeadChip assay was used to measure cytosine ('CpG' or 'cg') methylation levels at loci throughout the placental genome. Differential methylation was assessed by comparing the β-values (a measure of the extent of cytosine methylation) for individual CpG loci in fetuses with TOF vs in controls. The most discriminating CpG sites were determined based on a preset cut-off of ≥ 2.0-fold change in the methylation level. The predictive accuracy of CpG loci with significant methylation changes for TOF was determined by the area under the receiver-operating-characteristics curve (AUC). A false-discovery-rate (FDR) P-value < 0.05 was used to define a statistically significant difference in the methylation level. Ingenuity Pathway Analysis (IPA) (Qiagen) was used to identify gene pathways that were significantly overexpressed, and thus altered, in TOF cases compared with controls.

RESULTS

We found a total of 165 significantly differentially methylated CpG loci in TOF cases compared with controls, in 165 separate genes. These biomarkers demonstrated from fair to excellent individual predictive accuracy for TOF detection, with AUCs ≥ 0.75 (FDR P-value < 0.001 for all). The following CpG loci (gene) had the highest predictive accuracy: cg05273049 (ARHGAP22; AUC = 1.00; 95% CI, 1.00-1.00), cg02540011 (CDK5; AUC = 0.96; 95% CI, 0.87-1.00), cg08404201 (TRIM27; AUC = 0.95; 95% CI, 0.84-1.00) and cg00687252 (IER3; AUC = 0.95; 95% CI, 0.84-1.00). IPA revealed over-representation (dysregulation) of 14 gene pathways involved in normal cardiac development, including cardiomyocyte differentiation via bone morphogenetic protein receptors, cardiac hypertrophy signaling and role of nuclear factor of activated T cells in cardiac hypertrophy. Cardiac hypertrophy is an important feature of TOF.

CONCLUSIONS

Analysis of placental DNA cytosine methylation changes yielded accurate markers for TOF detection and provided mechanistic information on TOF development. Our work appears to confirm the central role of epigenetic changes and of the placenta in the development of TOF. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

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.

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.

Placental epigenetics for evaluation of fetal congenital heart defects: Ventricular Septal Defect (VSD)

Ventricular Septal Defect (VSD), the most common congenital heart defect, is characterized by a hole in the septum between the right and left ventricles. The pathogenesis of VSD is unknown in most clinical cases. There is a paucity of data relevant to epigenetic changes in VSD. The placenta is a fetal tissue crucial in cardiac development and a potentially useful surrogate for evaluating the development of heart tissue. To understand epigenetic mechanisms that may play a role in the development of VSD, genome-wide DNA methylation assay on placentas of 8 term subjects with isolated VSD and no known or suspected genetic syndromes and 10 unaffected controls was performed using the Illumina HumanMethylation450 BeadChip assay. We identified a total of 80 highly accurate potential CpGs in 80 genes for detection of VSD; area under the receiver operating characteristic curve (AUC ROC) 1.0 with significant 95% CI (FDR) p-values < 0.05 for each individual locus. The biological processes and functions for many of these differentially methylated genes are previously known to be associated with heart development or disease, including cardiac ventricle development (HEY2, ISL1), heart looping (SRF), cardiac muscle cell differentiation (ACTC1, HEY2), cardiac septum development (ISL1), heart morphogenesis (SRF, HEY2, ISL1, HEYL), Notch signaling pathway (HEY2, HEYL), cardiac chamber development (ISL1), and cardiac muscle tissue development (ACTC1, ISL1). In addition, we identified 8 microRNAs that have the potential to be biomarkers for the detection of VSD including: miR-191, miR-548F1, miR-148A, miR-423, miR-92B, miR-611, miR-2110, and miR-548H4. To our knowledge this is the first report in which placental analysis has been used for determining the pathogenesis of and predicting VSD.

Putative Systemic Biomarkers of Biomass Smoke-Induced Chronic Obstructive Pulmonary Disease among Women in a Rural South Indian Population

RATIONALE

Exposure to biomass smoke (BMS) has been implicated in chronic obstructive pulmonary disease (COPD). About 3 billion people worldwide use biomass fuel for cooking and heating. Women in rural communities of low- and lower-middle-income countries are disproportionately exposed to massive amounts of BMS during active cooking hours (4-6 h/day). Therefore, BMS exposure is considered as a risk factor for COPD in the same order of magnitude as tobacco smoke. In rural India, due to cultural reasons, women are the primary cook of the family and are mostly nonsmokers. Thus, BMS-induced COPD is predominant among rural Indian women. However, BMS-COPD remains a relatively unexplored health problem globally. Therefore, we investigated the serum chemokine and cytokine signatures of BMS-COPD and tobacco smoke-induced COPD (TS-COPD) patients compared to their control in a rural South Indian population for this field study.

METHODS

Concentrations of 40 serum chemokines and cytokines were measured using a multiplexed immunoassay. The study cohort consisted of BMS-COPD (female; n = 29) and BMS-exposed subjects without COPD (BMS-CONTROL; female; n = 24). For comparison, data from TS-COPD patients (male, n = 23) and tobacco smokers without COPD (TS-CONTROL; male, n = 22) were investigated. Subjects were matched for age, sex, and biomass exposure. Tobacco consumption was slightly higher in TS-COPD subjects compared to TS-CONTROL. BMS-exposed and TS-exposed subjects (currently exposed) were from the same locality with similar dwelling habits and socioeconomic status. A validated structured questionnaire-based survey and spirometry was performed. An additional control group with no tobacco and BMS exposure (TS-BMS-CONTROL; n = 15) was included. Statistical significance was set at p ≤ 0.01.

RESULTS

Serum median concentrations (pg/ml) of CCL15 [8799.35; 5977.22], CCL27 [1409.14; 1024.99], and CXCL13 [37.14; 26.03] were significantly higher in BMS-CONTROL compared to BMS-COPD subjects. Nine analytes exhibited higher concentrations in TS-CONTROL compared to TS-COPD subjects. Comparison of chemokine and cytokine concentrations among BMS-COPD versus TS-COPD and BMS-CONTROL versus TS-CONTROL subjects also revealed distinct molecular signatures.

CONCLUSION

Our data identifies CCL27 and CXCL13 as putative, plausibly homeostatic/protective biomarkers for BMS-COPD within the investigated population that warrants validation in larger and multiple cohorts. The findings further indicate exposure-specific systemic response of chemokines and cytokines.

Newborn blood DNA epigenetic variations and signaling pathway genes associated with Tetralogy of Fallot (TOF)

Tetralogy of Fallot (TOF) is the most common Critical Congenital Heart Defect (CCHD). The etiology of TOF is unknown in most cases. Preliminary data from our group and others suggest that epigenetic changes may play an important role in CHD. Epidemiologically, a significant percentage of CHD including TOF fail to be diagnosed in the prenatal and early newborn period which can negatively affect health outcomes. We performed genome-wide methylation assay in newborn blood in 24 non-syndromic TOF cases and 24 unaffected matched controls using Illumina Infinium HumanMethylation450 BeadChips. We identified 64 significantly differentially methylated CpG sites in TOF cases, of which 25 CpG sites had high predictive accuracy for TOF, based on the area under the receiver operating characteristics curve (AUC ROC) ≥ 0.90). The CpG methylation difference between TOF and controls was ≥10% in 51 CpG targets suggesting biological significance. Gene ontology analysis identified significant biological processes and functions related to these differentially methylated genes, including: CHD development, cardiomyopathy, diabetes, immunological, inflammation and other plausible pathways in CHD development. Multiple genes known or plausibly linked to heart development and post-natal heart disease were found to be differentially methylated in the blood DNA of newborns with TOF including: ABCB1, PPP2R5C, TLR1, SELL, SCN3A, CREM, RUNX and LHX9. We generated novel and highly accurate putative molecular markers for TOF detection using leucocyte DNA and thus provided information on pathogenesis of TOF.

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.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0795-y) contains supplementary material, which is available to authorized users.

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.

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 β2 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.

Global spectrum of copy number variations reveals genome organizational plasticity and proposes new migration routes

Global spectrum of CNVs is required to catalog variations to provide a high-resolution on the dynamics of genome-organization and human migration. In this study, we performed genome-wide genotyping using high-resolution arrays and identified 44,109 CNVs from 1,715 genomes across 12 populations. The study unraveled the force of independent evolutionary dynamics on genome-organizational plasticity across populations. We demonstrated the use of CNV tool to study human migration and identified a second major settlement establishing new migration routes in addition to existing ones.

Copy number variation burden on asthma subgenome in normal cohorts identifies susceptibility markers

PURPOSE

Asthma is a complex disease caused by interplay of genes and environment on the genome of an individual. Copy number variations (CNVs) are more common compared to the other variations that disrupt genome organization. The effect of CNVs on asthma subgenome has been less studied compared to studies on the other variations. We report the assessments of CNV burden in asthma genes of normal cohorts carried out in different geographical areas of the world and discuss the relevance of the observation with respect to asthma pathogenesis.

METHODS

CNV analysis was performed using Affymerix high-resolution arrays, and various bioinformatics tools were used to understand the influence of genes on asthma pathogenesis.

RESULTS

This study identified 61 genes associated with asthma and provided various mechanisms and pathways underlying asthma pathogenesis. CCL3L1, ADAM8, and MUC5B were the most prevalent asthma genes. Among them, CCL3L1 was found across all 12 populations in varying copy number states. This study also identified the inheritance of asthma-CNVs from parents to offspring creating the latent period for manifestation of asthma.

CONCLUSIONS

This study revealed CNV burden with varying copy number states and identified susceptibility towards the disease manifestation. It can be hypothesized that primary CNVs may not be the initiating event in the pathogenesis of asthma and additional preceding mutations or CNVs may be required. The initiator or primary CNVs sensitize normal cohorts leading to an increased probability of accumulating mutations or exposure to allergic stimulating agents that can augment the development of asthma.

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.

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 β (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.

Copy number variations burden on miRNA genes reveals layers of complexities involved in the regulation of pathways and phenotypic expression

MicroRNAs are involved in post-transcriptional down-regulation of gene expression. Variations in miRNA genes can severely affect downstream-regulated genes and their pathways. However, population-specific burden of CNVs on miRNA genes and the complexities created towards the phenotype is not known. From a total of 44109 CNVs investigated from 1715 individuals across 12 populations using high-throughput arrays, 4007 miRNA-CNVs (∼ 9%) consisting 6542 (∼ 5%) miRNA genes with a total of 333 (∼ 5%) singleton miRNA genes were identified. We found miRNA-CNVs across the genomes of individuals showing multiple hits in many targets, co-regulated under the same pathway. This study proposes four mechanisms unraveling the many complexities in miRNA genes, targets and co-regulated miRNA genes towards establishment of phenotypic diversity.

Unravelling the complexity of human olfactory receptor repertoire by copy number analysis across population using high resolution arrays

Olfactory receptors (OR), responsible for detection of odor molecules, belong to the largest family of genes and are highly polymorphic in nature having distinct polymorphisms associated with specific regions around the globe. Since there are no reports on the presence of copy number variations in OR repertoire of Indian population, the present investigation in 43 Indians along with 270 HapMap and 31 Tibetan samples was undertaken to study genome variability and evolution. Analysis was performed using Affymetrix Genome-Wide Human SNP Array 6.0 chip, Affymterix CytoScan^® High-Density array, HD-CNV, and MAFFT program. We observed a total of 1527 OR genes in 503 CNV events from 81.3% of the study group, which includes 67.6% duplications and 32.4% deletions encompassing more of genes than pseudogenes. We report human genotypic variation in functional OR repertoire size across populations and it was found that the combinatorial effect of both “orthologous obtained from closely related species” and “paralogous derived sequences” provide the complexity to the continuously occurring OR CNVs.

Identification of a novel mutation in the CDHR1 gene in a family with recessive retinal degeneration

OBJECTIVES

To describe the clinical phenotype and identify the molecular basis of disease in a consanguineous family of Palestinian origin with autosomal recessive retinal degeneration.

METHODS

Eight family members were evaluated with visual acuity and perimetry tests, color fundus photographs, full-field electroretinography, and optical coherence tomography. Cone photoreceptors surrounding the fovea were imaged in 2 members, using adaptive optics scanning laser ophthalmoscopy. Exome was captured using probes and sequenced. Readings were mapped to reference hg19. Variant calls and annotations were performed, using published protocols. Confirmation of variants and segregation analysis was performed using dideoxy sequencing.

RESULTS

Analysis detected 24 037 single-nucleotide variants in one affected family member, of which 3622 were rare and potentially damaging to encoded proteins. Further analysis revealed a novel homozygous nonsense change, c.1381 C>T, p.Gln461X in exon 13 of the CDHR1 gene, which segregated with retinal degeneration in this family. Affected members had night blindness beginning during adolescence with progressive visual acuity and field loss and unmeasurable electroretinographic responses, as well as macular outer retinal loss, although residual cones with increased cone spacing were observed in the youngest individual.

CONCLUSIONS

Exome analysis revealed a novel CDHR1 nonsense mutation segregating with progressive retinal degeneration causing severe central vision loss by the fourth decade of life. High-resolution retinal imaging revealed outer retinal changes suggesting that CDHR1 is important for normal photoreceptor structure and survival.

CLINICAL RELEVANCE

Exome sequencing is a powerful technique that may identify causative genetic variants in families with autosomal recessive retinal degeneration.